emotion_mf_time_series.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59337dd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ede664c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams.update({'font.size': 22})\n",
    "from matplotlib.dates import DateFormatter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2082119",
   "metadata": {},
   "source": [
    "# data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "18532333",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fionaguo/opt/anaconda3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3444: DtypeWarning: Columns (0,2,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "# import data - already aggregated hourly\n",
    "names=['index','time','political','state','num_tot_tweets','num_tot_retweets',\n",
    "       'bias_sanctity', 'bias_fairness', 'bias_authority','bias_loyalty', 'bias_care',\n",
    "       'intensity_sanctity', 'intensity_fairness','intensity_authority', 'intensity_loyalty', 'intensity_care',\n",
    "       'vice_sanctity', 'virtue_sanctity', 'vice_fairness', 'virtue_fairness',\n",
    "       'vice_authority', 'virtue_authority', 'vice_loyalty', 'virtue_loyalty','vice_care', 'virtue_care',\n",
    "       'anger','anticipation','disgust','fear','joy','love','optimism','pessimism','sadness','surprise','trust']\n",
    "\n",
    "df = pd.read_csv('agg_covid_tweets_states.csv',names=names,lineterminator='\\n')\n",
    "df = df.drop_duplicates()\n",
    "df = df.drop(0,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2a90dce7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>political</th>\n",
       "      <th>state</th>\n",
       "      <th>num_tot_tweets</th>\n",
       "      <th>num_tot_retweets</th>\n",
       "      <th>bias_sanctity</th>\n",
       "      <th>bias_fairness</th>\n",
       "      <th>bias_authority</th>\n",
       "      <th>bias_loyalty</th>\n",
       "      <th>bias_care</th>\n",
       "      <th>...</th>\n",
       "      <th>anticipation</th>\n",
       "      <th>disgust</th>\n",
       "      <th>fear</th>\n",
       "      <th>joy</th>\n",
       "      <th>love</th>\n",
       "      <th>optimism</th>\n",
       "      <th>pessimism</th>\n",
       "      <th>sadness</th>\n",
       "      <th>surprise</th>\n",
       "      <th>trust</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-24 00:00:00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>AK</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.115558</td>\n",
       "      <td>-0.085331</td>\n",
       "      <td>-0.093436</td>\n",
       "      <td>-0.090459</td>\n",
       "      <td>-0.107172</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-24 00:00:00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>AL</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-0.079721</td>\n",
       "      <td>0.001060</td>\n",
       "      <td>-0.028805</td>\n",
       "      <td>-0.039228</td>\n",
       "      <td>-0.007037</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24 00:00:00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>AR</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-0.030927</td>\n",
       "      <td>0.035619</td>\n",
       "      <td>-0.017028</td>\n",
       "      <td>-0.016343</td>\n",
       "      <td>0.042674</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-24 00:00:00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>AZ</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.061938</td>\n",
       "      <td>-0.052352</td>\n",
       "      <td>-0.045468</td>\n",
       "      <td>-0.042103</td>\n",
       "      <td>-0.067456</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-24 00:00:00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>235.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>-0.071316</td>\n",
       "      <td>-0.046673</td>\n",
       "      <td>-0.052038</td>\n",
       "      <td>-0.050194</td>\n",
       "      <td>-0.056694</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072340</td>\n",
       "      <td>0.302128</td>\n",
       "      <td>0.212766</td>\n",
       "      <td>0.038298</td>\n",
       "      <td>0.004255</td>\n",
       "      <td>0.029787</td>\n",
       "      <td>0.008511</td>\n",
       "      <td>0.097872</td>\n",
       "      <td>0.008511</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242927</th>\n",
       "      <td>2020-04-30 23:00:00</td>\n",
       "      <td>moderacy1</td>\n",
       "      <td>all</td>\n",
       "      <td>1929.0</td>\n",
       "      <td>1091.0</td>\n",
       "      <td>-0.000872</td>\n",
       "      <td>0.020250</td>\n",
       "      <td>0.002298</td>\n",
       "      <td>0.010440</td>\n",
       "      <td>0.029954</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058580</td>\n",
       "      <td>0.348367</td>\n",
       "      <td>0.053914</td>\n",
       "      <td>0.161742</td>\n",
       "      <td>0.008294</td>\n",
       "      <td>0.111457</td>\n",
       "      <td>0.004666</td>\n",
       "      <td>0.089684</td>\n",
       "      <td>0.011923</td>\n",
       "      <td>0.002592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242928</th>\n",
       "      <td>2020-04-30 23:00:00</td>\n",
       "      <td>political-1</td>\n",
       "      <td>all</td>\n",
       "      <td>1833.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>-0.000136</td>\n",
       "      <td>0.021395</td>\n",
       "      <td>0.002642</td>\n",
       "      <td>0.011030</td>\n",
       "      <td>0.031117</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054010</td>\n",
       "      <td>0.352973</td>\n",
       "      <td>0.053464</td>\n",
       "      <td>0.162029</td>\n",
       "      <td>0.009274</td>\n",
       "      <td>0.110202</td>\n",
       "      <td>0.004364</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.009820</td>\n",
       "      <td>0.002728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242929</th>\n",
       "      <td>2020-04-30 23:00:00</td>\n",
       "      <td>political1</td>\n",
       "      <td>all</td>\n",
       "      <td>919.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>-0.007291</td>\n",
       "      <td>-0.000457</td>\n",
       "      <td>0.000511</td>\n",
       "      <td>0.004389</td>\n",
       "      <td>0.013149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046790</td>\n",
       "      <td>0.365615</td>\n",
       "      <td>0.058760</td>\n",
       "      <td>0.150163</td>\n",
       "      <td>0.011970</td>\n",
       "      <td>0.100109</td>\n",
       "      <td>0.007617</td>\n",
       "      <td>0.076170</td>\n",
       "      <td>0.023939</td>\n",
       "      <td>0.002176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242930</th>\n",
       "      <td>2020-04-30 23:00:00</td>\n",
       "      <td>science-1</td>\n",
       "      <td>all</td>\n",
       "      <td>888.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>-0.007374</td>\n",
       "      <td>-0.000198</td>\n",
       "      <td>0.000472</td>\n",
       "      <td>0.004071</td>\n",
       "      <td>0.013349</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045045</td>\n",
       "      <td>0.365991</td>\n",
       "      <td>0.059685</td>\n",
       "      <td>0.141892</td>\n",
       "      <td>0.010135</td>\n",
       "      <td>0.097973</td>\n",
       "      <td>0.007883</td>\n",
       "      <td>0.074324</td>\n",
       "      <td>0.025901</td>\n",
       "      <td>0.002252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242931</th>\n",
       "      <td>2020-04-30 23:00:00</td>\n",
       "      <td>science1</td>\n",
       "      <td>all</td>\n",
       "      <td>1864.0</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>-0.000215</td>\n",
       "      <td>0.020908</td>\n",
       "      <td>0.002626</td>\n",
       "      <td>0.011071</td>\n",
       "      <td>0.030723</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054721</td>\n",
       "      <td>0.353004</td>\n",
       "      <td>0.053112</td>\n",
       "      <td>0.165773</td>\n",
       "      <td>0.010193</td>\n",
       "      <td>0.111052</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>0.090665</td>\n",
       "      <td>0.009120</td>\n",
       "      <td>0.002682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242932 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      time    political state  num_tot_tweets  \\\n",
       "0      2020-01-24 00:00:00         -1.0    AK             8.0   \n",
       "1      2020-01-24 00:00:00         -1.0    AL            17.0   \n",
       "2      2020-01-24 00:00:00         -1.0    AR             5.0   \n",
       "3      2020-01-24 00:00:00         -1.0    AZ            22.0   \n",
       "4      2020-01-24 00:00:00         -1.0    CA           235.0   \n",
       "...                    ...          ...   ...             ...   \n",
       "242927 2020-04-30 23:00:00    moderacy1   all          1929.0   \n",
       "242928 2020-04-30 23:00:00  political-1   all          1833.0   \n",
       "242929 2020-04-30 23:00:00   political1   all           919.0   \n",
       "242930 2020-04-30 23:00:00    science-1   all           888.0   \n",
       "242931 2020-04-30 23:00:00     science1   all          1864.0   \n",
       "\n",
       "        num_tot_retweets  bias_sanctity  bias_fairness  bias_authority  \\\n",
       "0                    6.0      -0.115558      -0.085331       -0.093436   \n",
       "1                   13.0      -0.079721       0.001060       -0.028805   \n",
       "2                    3.0      -0.030927       0.035619       -0.017028   \n",
       "3                   20.0      -0.061938      -0.052352       -0.045468   \n",
       "4                  178.0      -0.071316      -0.046673       -0.052038   \n",
       "...                  ...            ...            ...             ...   \n",
       "242927            1091.0      -0.000872       0.020250        0.002298   \n",
       "242928            1034.0      -0.000136       0.021395        0.002642   \n",
       "242929             546.0      -0.007291      -0.000457        0.000511   \n",
       "242930             540.0      -0.007374      -0.000198        0.000472   \n",
       "242931            1040.0      -0.000215       0.020908        0.002626   \n",
       "\n",
       "        bias_loyalty  bias_care  ...  anticipation   disgust      fear  \\\n",
       "0          -0.090459  -0.107172  ...      0.000000  0.125000  0.250000   \n",
       "1          -0.039228  -0.007037  ...      0.000000  0.117647  0.176471   \n",
       "2          -0.016343   0.042674  ...      0.200000  0.200000  0.200000   \n",
       "3          -0.042103  -0.067456  ...      0.045455  0.318182  0.363636   \n",
       "4          -0.050194  -0.056694  ...      0.072340  0.302128  0.212766   \n",
       "...              ...        ...  ...           ...       ...       ...   \n",
       "242927      0.010440   0.029954  ...      0.058580  0.348367  0.053914   \n",
       "242928      0.011030   0.031117  ...      0.054010  0.352973  0.053464   \n",
       "242929      0.004389   0.013149  ...      0.046790  0.365615  0.058760   \n",
       "242930      0.004071   0.013349  ...      0.045045  0.365991  0.059685   \n",
       "242931      0.011071   0.030723  ...      0.054721  0.353004  0.053112   \n",
       "\n",
       "             joy      love  optimism  pessimism   sadness  surprise     trust  \n",
       "0       0.000000  0.000000  0.000000   0.000000  0.000000  0.000000  0.000000  \n",
       "1       0.000000  0.000000  0.235294   0.000000  0.000000  0.000000  0.000000  \n",
       "2       0.200000  0.000000  0.200000   0.000000  0.200000  0.000000  0.000000  \n",
       "3       0.000000  0.000000  0.000000   0.000000  0.090909  0.000000  0.000000  \n",
       "4       0.038298  0.004255  0.029787   0.008511  0.097872  0.008511  0.000000  \n",
       "...          ...       ...       ...        ...       ...       ...       ...  \n",
       "242927  0.161742  0.008294  0.111457   0.004666  0.089684  0.011923  0.002592  \n",
       "242928  0.162029  0.009274  0.110202   0.004364  0.090016  0.009820  0.002728  \n",
       "242929  0.150163  0.011970  0.100109   0.007617  0.076170  0.023939  0.002176  \n",
       "242930  0.141892  0.010135  0.097973   0.007883  0.074324  0.025901  0.002252  \n",
       "242931  0.165773  0.010193  0.111052   0.004292  0.090665  0.009120  0.002682  \n",
       "\n",
       "[242932 rows x 36 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# during aggregation, some dates index is included, some dates it's not, normalize the data\n",
    "tmp = df[df['trust'].isnull()]\n",
    "tmp.drop('trust',axis=1,inplace=True)\n",
    "\n",
    "names.remove('index')\n",
    "tmp.columns = names\n",
    "\n",
    "df = df[~df['trust'].isnull()]\n",
    "df.drop('index',axis=1,inplace=True)\n",
    "\n",
    "df = pd.concat([df,tmp],axis=0)\n",
    "\n",
    "df = df[~df.anticipation.isnull()]\n",
    "\n",
    "df.time = pd.to_datetime(df.time)\n",
    "\n",
    "# set time frame to 01/24 to 05/01\n",
    "df = df[(df.time >= pd.Timestamp('2020-01-24')) & (df.time < pd.Timestamp('2020-05-01'))]\n",
    "\n",
    "df = df.set_index(['time','political','state'])\n",
    "df = df.astype(float)\n",
    "\n",
    "df = df.sort_index(level=0)\n",
    "\n",
    "df = df.reset_index()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e70bfb5",
   "metadata": {},
   "source": [
    "# plot emotion and MF time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5baf1c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# first look at all subpopulations, no partiton on political ideologies or US states\n",
    "df_all = df[(df.political=='all')&(df.state=='all')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b4d0e4b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all.to_csv('covid_tweets_emotions_mf_daily_agg.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "c885ab3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate daily aggregation - less noise on the figure\n",
    "var_names = ['bias_sanctity', 'bias_fairness', 'bias_authority','bias_loyalty', 'bias_care',\n",
    "       'intensity_sanctity', 'intensity_fairness','intensity_authority', 'intensity_loyalty', 'intensity_care',\n",
    "       'vice_sanctity', 'virtue_sanctity', 'vice_fairness', 'virtue_fairness',\n",
    "       'vice_authority', 'virtue_authority', 'vice_loyalty', 'virtue_loyalty','vice_care', 'virtue_care',\n",
    "       'anger','anticipation','disgust','fear','joy','love','optimism','pessimism','sadness','surprise','trust']\n",
    "\n",
    "df_all_daily = df_all.copy(deep=True)\n",
    "\n",
    "for var in var_names:\n",
    "    df_all_daily[var] = df_all_daily[var]*df_all_daily['num_tot_tweets']\n",
    "    \n",
    "df_all_daily['date'] = df_all_daily.time.dt.date\n",
    "\n",
    "df_all_daily = df_all_daily.groupby('date')[['num_tot_tweets']+var_names].sum()\n",
    "\n",
    "for var in var_names:\n",
    "    df_all_daily[var] = df_all_daily[var]/df_all_daily['num_tot_tweets']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "91fc3a23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot moral foundation\n",
    "\n",
    "\n",
    "for axis in ['bias']:\n",
    "    fig,ax = plt.subplots(figsize=[10,4])\n",
    "    for var,lt in zip(['loyalty','authority','care','sanctity','fairness'],['-.','--','-',':','.']):\n",
    "        p = plt.plot(df_all_daily.loc[df_all_daily.index>=pd.Timestamp('2020-01-24'),axis+'_'+var],lt,label=var)\n",
    "#     for e in events:\n",
    "#         p1 = plt.axvline(x=pd.Timestamp(e[0]),color=\"black\",label=e[1])\n",
    "    plt.axhline(y=0,color='black')\n",
    "    #plt.axvline(x=pd.Timestamp('2020-02-28'),color='black')\n",
    "    #plt.legend(bbox_to_anchor=(0.5,-1),ncol=5)\n",
    "    plt.ylim((-0.06,0.05))\n",
    "    plt.xticks(rotation = 0)\n",
    "    plt.ylabel(axis)\n",
    "    # ax.xaxis.set_major_locator(plt.MaxNLocator(6))\n",
    "    ax.xaxis.set_ticks([pd.Timestamp('2020-02-01'),pd.Timestamp('2020-03-01'),pd.Timestamp('2020-04-01'),pd.Timestamp('2020-05-01')])\n",
    "    date_form = DateFormatter(\"%m-%d\")\n",
    "    ax.xaxis.set_major_formatter(date_form)\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "for axis in ['intensity']:\n",
    "    fig,ax = plt.subplots(figsize=[10,4])\n",
    "    for var,lt in zip(['loyalty','authority','care','sanctity','fairness'],['-.','--','-',':','.']):\n",
    "        p = plt.plot(df_all_daily.loc[df_all_daily.index>=pd.Timestamp('2020-01-24'),axis+'_'+var],lt,label=var)\n",
    "#     for e in events:\n",
    "#         p1 = plt.axvline(x=pd.Timestamp(e[0]),color=\"black\",label=e[1])\n",
    "    # plt.axhline(y=0,color='black')\n",
    "    #plt.axvline(x=pd.Timestamp('2020-01-30'),color='black')\n",
    "    # plt.legend(loc=\"lower center\",mode='expand')\n",
    "    plt.ylim((0.013,0.033))\n",
    "    plt.xticks(rotation = 0)\n",
    "    plt.ylabel(axis)\n",
    "    date_form = DateFormatter(\"%m-%d\")\n",
    "    ax.xaxis.set_major_formatter(date_form)\n",
    "    ax.xaxis.set_ticks([pd.Timestamp('2020-02-01'),pd.Timestamp('2020-03-01'),pd.Timestamp('2020-04-01'),pd.Timestamp('2020-05-01')])\n",
    "    # plt.title('Bi')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "08239afc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "love\n",
      "pessimism\n",
      "surprise\n",
      "trust\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot emotions\n",
    "emot_colnames = ['anger','disgust','fear','joy','love','optimism','pessimism','surprise','trust'] # flat not intereseting - sadness, anticipation\n",
    "colors = ['brown','olive','orangered','gold','magenta','purple','teal','black','grey','pink']\n",
    "linetype = ['--','-.','-','-o',':','-.','-x','-^','-<','->']\n",
    "\n",
    "fig,ax = plt.subplots(figsize=[10,4])\n",
    "for axis,color,lt in zip(emot_colnames,colors,linetype):\n",
    "    if df_all_daily[axis].max() < 0.05: # don't show - otherwise too noisy\n",
    "        print(axis)\n",
    "        continue\n",
    "    p = plt.plot(df_all_daily[axis],lt,label=axis,color=color)\n",
    "#     for e in events:\n",
    "#         p1 = plt.axvline(x=pd.Timestamp(e[0]),color=\"black\",label=e[1])\n",
    "\n",
    "#plt.legend(bbox_to_anchor=(0.5,-0.1),ncol=5)\n",
    "plt.xticks(rotation = 0)\n",
    "# plt.xlim((pd.Timestamp('2020-03-01'),pd.Timestamp('2020-03-31')))\n",
    "# plt.ylim((0,0.5))\n",
    "# plt.yscale('log')\n",
    "plt.ylabel('Fraction of tweets')\n",
    "# plt.title(group[0]+' population')\n",
    "from matplotlib.dates import DateFormatter\n",
    "date_form = DateFormatter(\"%m-%d\")\n",
    "ax.xaxis.set_major_formatter(date_form)\n",
    "ax.xaxis.set_ticks([pd.Timestamp('2020-02-01'),pd.Timestamp('2020-03-01'),pd.Timestamp('2020-04-01'),pd.Timestamp('2020-05-01')])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28d32a45",
   "metadata": {},
   "source": [
    "# Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f90e6c94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# emotions extracted using EmoLex method\n",
    "df_emolex = pd.read_csv('covid_tweets_random_sampled_5perc_emolex_numerical_score.csv')\n",
    "df_emolex['date'] = pd.to_datetime(df_emolex['date'])\n",
    "df_emolex = df_emolex.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "363d8e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anger_emolex</th>\n",
       "      <th>anticipation_emolex</th>\n",
       "      <th>disgust_emolex</th>\n",
       "      <th>fear_emolex</th>\n",
       "      <th>joy_emolex</th>\n",
       "      <th>love_emolex</th>\n",
       "      <th>sadness_emolex</th>\n",
       "      <th>surprise_emolex</th>\n",
       "      <th>trust_emolex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0.009696</td>\n",
       "      <td>0.010521</td>\n",
       "      <td>0.011033</td>\n",
       "      <td>0.017381</td>\n",
       "      <td>0.007943</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014300</td>\n",
       "      <td>0.007465</td>\n",
       "      <td>0.018375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0.009658</td>\n",
       "      <td>0.013297</td>\n",
       "      <td>0.010557</td>\n",
       "      <td>0.017050</td>\n",
       "      <td>0.009354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014308</td>\n",
       "      <td>0.008885</td>\n",
       "      <td>0.018698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0.009246</td>\n",
       "      <td>0.016500</td>\n",
       "      <td>0.010014</td>\n",
       "      <td>0.017986</td>\n",
       "      <td>0.010485</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015082</td>\n",
       "      <td>0.009578</td>\n",
       "      <td>0.020041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>0.010783</td>\n",
       "      <td>0.011422</td>\n",
       "      <td>0.011080</td>\n",
       "      <td>0.018180</td>\n",
       "      <td>0.005496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012700</td>\n",
       "      <td>0.008254</td>\n",
       "      <td>0.016994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-28</th>\n",
       "      <td>0.011789</td>\n",
       "      <td>0.012486</td>\n",
       "      <td>0.011403</td>\n",
       "      <td>0.017433</td>\n",
       "      <td>0.007159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013962</td>\n",
       "      <td>0.008872</td>\n",
       "      <td>0.018514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-26</th>\n",
       "      <td>0.013048</td>\n",
       "      <td>0.011713</td>\n",
       "      <td>0.011520</td>\n",
       "      <td>0.017918</td>\n",
       "      <td>0.007728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016058</td>\n",
       "      <td>0.007128</td>\n",
       "      <td>0.017828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-27</th>\n",
       "      <td>0.013503</td>\n",
       "      <td>0.012229</td>\n",
       "      <td>0.011147</td>\n",
       "      <td>0.018279</td>\n",
       "      <td>0.007587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015496</td>\n",
       "      <td>0.006689</td>\n",
       "      <td>0.017933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-28</th>\n",
       "      <td>0.012924</td>\n",
       "      <td>0.012537</td>\n",
       "      <td>0.011088</td>\n",
       "      <td>0.018218</td>\n",
       "      <td>0.007407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015421</td>\n",
       "      <td>0.006647</td>\n",
       "      <td>0.018016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-29</th>\n",
       "      <td>0.013142</td>\n",
       "      <td>0.012619</td>\n",
       "      <td>0.011355</td>\n",
       "      <td>0.017939</td>\n",
       "      <td>0.007448</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015745</td>\n",
       "      <td>0.006653</td>\n",
       "      <td>0.017654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-30</th>\n",
       "      <td>0.013182</td>\n",
       "      <td>0.012687</td>\n",
       "      <td>0.010808</td>\n",
       "      <td>0.018142</td>\n",
       "      <td>0.007818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015876</td>\n",
       "      <td>0.006714</td>\n",
       "      <td>0.017866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            anger_emolex  anticipation_emolex  disgust_emolex  fear_emolex  \\\n",
       "date                                                                         \n",
       "2020-01-24      0.009696             0.010521        0.011033     0.017381   \n",
       "2020-01-25      0.009658             0.013297        0.010557     0.017050   \n",
       "2020-01-26      0.009246             0.016500        0.010014     0.017986   \n",
       "2020-01-27      0.010783             0.011422        0.011080     0.018180   \n",
       "2020-01-28      0.011789             0.012486        0.011403     0.017433   \n",
       "...                  ...                  ...             ...          ...   \n",
       "2020-04-26      0.013048             0.011713        0.011520     0.017918   \n",
       "2020-04-27      0.013503             0.012229        0.011147     0.018279   \n",
       "2020-04-28      0.012924             0.012537        0.011088     0.018218   \n",
       "2020-04-29      0.013142             0.012619        0.011355     0.017939   \n",
       "2020-04-30      0.013182             0.012687        0.010808     0.018142   \n",
       "\n",
       "            joy_emolex  love_emolex  sadness_emolex  surprise_emolex  \\\n",
       "date                                                                   \n",
       "2020-01-24    0.007943          0.0        0.014300         0.007465   \n",
       "2020-01-25    0.009354          0.0        0.014308         0.008885   \n",
       "2020-01-26    0.010485          0.0        0.015082         0.009578   \n",
       "2020-01-27    0.005496          0.0        0.012700         0.008254   \n",
       "2020-01-28    0.007159          0.0        0.013962         0.008872   \n",
       "...                ...          ...             ...              ...   \n",
       "2020-04-26    0.007728          0.0        0.016058         0.007128   \n",
       "2020-04-27    0.007587          0.0        0.015496         0.006689   \n",
       "2020-04-28    0.007407          0.0        0.015421         0.006647   \n",
       "2020-04-29    0.007448          0.0        0.015745         0.006653   \n",
       "2020-04-30    0.007818          0.0        0.015876         0.006714   \n",
       "\n",
       "            trust_emolex  \n",
       "date                      \n",
       "2020-01-24      0.018375  \n",
       "2020-01-25      0.018698  \n",
       "2020-01-26      0.020041  \n",
       "2020-01-27      0.016994  \n",
       "2020-01-28      0.018514  \n",
       "...                  ...  \n",
       "2020-04-26      0.017828  \n",
       "2020-04-27      0.017933  \n",
       "2020-04-28      0.018016  \n",
       "2020-04-29      0.017654  \n",
       "2020-04-30      0.017866  \n",
       "\n",
       "[97 rows x 9 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_emolex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "280fa708",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fionaguo/opt/anaconda3/lib/python3.9/site-packages/pandas/core/ops/array_ops.py:73: FutureWarning: Comparison of Timestamp with datetime.date is deprecated in order to match the standard library behavior.  In a future version these will be considered non-comparable.Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.\n",
      "  result = libops.scalar_compare(x.ravel(), y, op)\n"
     ]
    }
   ],
   "source": [
    "# vader sentiments\n",
    "vader = pd.read_csv('covid_tweets_random_sampled_5perc_vader.csv')\n",
    "vader.date = pd.to_datetime(vader.date).dt.date\n",
    "vader = vader[(vader.date >=pd.Timestamp('2020-01/24')) & (vader.date < pd.Timestamp('2020-05-01'))]\n",
    "vader.set_index('date',inplace=True)\n",
    "vader.columns = ['vader_sent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "ac895e4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fionaguo/opt/anaconda3/lib/python3.9/site-packages/pandas/core/dtypes/missing.py:483: FutureWarning: Comparison of Timestamp with datetime.date is deprecated in order to match the standard library behavior.  In a future version these will be considered non-comparable.Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.\n",
      "  return lib.array_equivalent_object(\n"
     ]
    }
   ],
   "source": [
    "# combine all results\n",
    "df_all_vars = pd.concat([df_all_daily,df_emolex,vader],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "04de5318",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = df_all_vars.corr(method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "14929678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_tot_tweets</th>\n",
       "      <th>bias_sanctity</th>\n",
       "      <th>bias_fairness</th>\n",
       "      <th>bias_authority</th>\n",
       "      <th>bias_loyalty</th>\n",
       "      <th>bias_care</th>\n",
       "      <th>intensity_sanctity</th>\n",
       "      <th>intensity_fairness</th>\n",
       "      <th>intensity_authority</th>\n",
       "      <th>intensity_loyalty</th>\n",
       "      <th>...</th>\n",
       "      <th>anger_emolex</th>\n",
       "      <th>anticipation_emolex</th>\n",
       "      <th>disgust_emolex</th>\n",
       "      <th>fear_emolex</th>\n",
       "      <th>joy_emolex</th>\n",
       "      <th>love_emolex</th>\n",
       "      <th>sadness_emolex</th>\n",
       "      <th>surprise_emolex</th>\n",
       "      <th>trust_emolex</th>\n",
       "      <th>vader_sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>num_tot_tweets</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006049</td>\n",
       "      <td>-0.005838</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.179637</td>\n",
       "      <td>0.055360</td>\n",
       "      <td>0.046221</td>\n",
       "      <td>-0.428992</td>\n",
       "      <td>-0.095532</td>\n",
       "      <td>-0.168525</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.274379</td>\n",
       "      <td>-0.349897</td>\n",
       "      <td>-0.192812</td>\n",
       "      <td>-0.481656</td>\n",
       "      <td>-0.143620</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.349306</td>\n",
       "      <td>0.125842</td>\n",
       "      <td>0.078345</td>\n",
       "      <td>-0.129957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bias_sanctity</th>\n",
       "      <td>-0.006049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.925981</td>\n",
       "      <td>0.842073</td>\n",
       "      <td>0.872765</td>\n",
       "      <td>0.927782</td>\n",
       "      <td>-0.858747</td>\n",
       "      <td>-0.404705</td>\n",
       "      <td>-0.611298</td>\n",
       "      <td>-0.485312</td>\n",
       "      <td>...</td>\n",
       "      <td>0.590114</td>\n",
       "      <td>-0.061843</td>\n",
       "      <td>0.077859</td>\n",
       "      <td>-0.018212</td>\n",
       "      <td>0.559410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.512860</td>\n",
       "      <td>-0.668394</td>\n",
       "      <td>0.286372</td>\n",
       "      <td>0.705147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bias_fairness</th>\n",
       "      <td>-0.005838</td>\n",
       "      <td>0.925981</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900813</td>\n",
       "      <td>0.882219</td>\n",
       "      <td>0.988060</td>\n",
       "      <td>-0.811001</td>\n",
       "      <td>-0.360983</td>\n",
       "      <td>-0.696074</td>\n",
       "      <td>-0.565156</td>\n",
       "      <td>...</td>\n",
       "      <td>0.550652</td>\n",
       "      <td>0.007693</td>\n",
       "      <td>-0.020316</td>\n",
       "      <td>-0.072020</td>\n",
       "      <td>0.540777</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.499632</td>\n",
       "      <td>-0.671076</td>\n",
       "      <td>0.297457</td>\n",
       "      <td>0.761835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bias_authority</th>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.842073</td>\n",
       "      <td>0.900813</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.923969</td>\n",
       "      <td>0.934134</td>\n",
       "      <td>-0.779521</td>\n",
       "      <td>-0.431964</td>\n",
       "      <td>-0.772854</td>\n",
       "      <td>-0.574256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.476331</td>\n",
       "      <td>-0.060699</td>\n",
       "      <td>-0.057082</td>\n",
       "      <td>-0.125618</td>\n",
       "      <td>0.461353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.413423</td>\n",
       "      <td>-0.529495</td>\n",
       "      <td>0.350476</td>\n",
       "      <td>0.686777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bias_loyalty</th>\n",
       "      <td>0.179637</td>\n",
       "      <td>0.872765</td>\n",
       "      <td>0.882219</td>\n",
       "      <td>0.923969</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.911793</td>\n",
       "      <td>-0.707264</td>\n",
       "      <td>-0.524182</td>\n",
       "      <td>-0.713299</td>\n",
       "      <td>-0.518633</td>\n",
       "      <td>...</td>\n",
       "      <td>0.407782</td>\n",
       "      <td>-0.203398</td>\n",
       "      <td>-0.072467</td>\n",
       "      <td>-0.228606</td>\n",
       "      <td>0.418972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.343349</td>\n",
       "      <td>-0.636309</td>\n",
       "      <td>0.194851</td>\n",
       "      <td>0.697914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bias_care</th>\n",
       "      <td>0.055360</td>\n",
       "      <td>0.927782</td>\n",
       "      <td>0.988060</td>\n",
       "      <td>0.934134</td>\n",
       "      <td>0.911793</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.830160</td>\n",
       "      <td>-0.376762</td>\n",
       "      <td>-0.714851</td>\n",
       "      <td>-0.566234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.554558</td>\n",
       "      <td>-0.018462</td>\n",
       "      <td>-0.017029</td>\n",
       "      <td>-0.097623</td>\n",
       "      <td>0.543407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.495805</td>\n",
       "      <td>-0.647920</td>\n",
       "      <td>0.321231</td>\n",
       "      <td>0.753813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intensity_sanctity</th>\n",
       "      <td>0.046221</td>\n",
       "      <td>-0.858747</td>\n",
       "      <td>-0.811001</td>\n",
       "      <td>-0.779521</td>\n",
       "      <td>-0.707264</td>\n",
       "      <td>-0.830160</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.310541</td>\n",
       "      <td>0.657387</td>\n",
       "      <td>0.550665</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.655218</td>\n",
       "      <td>-0.136033</td>\n",
       "      <td>-0.157677</td>\n",
       "      <td>-0.115874</td>\n",
       "      <td>-0.559015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.571284</td>\n",
       "      <td>0.524708</td>\n",
       "      <td>-0.459289</td>\n",
       "      <td>-0.584710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intensity_fairness</th>\n",
       "      <td>-0.428992</td>\n",
       "      <td>-0.404705</td>\n",
       "      <td>-0.360983</td>\n",
       "      <td>-0.431964</td>\n",
       "      <td>-0.524182</td>\n",
       "      <td>-0.376762</td>\n",
       "      <td>0.310541</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.534478</td>\n",
       "      <td>0.514202</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.118714</td>\n",
       "      <td>0.451925</td>\n",
       "      <td>-0.012900</td>\n",
       "      <td>0.311290</td>\n",
       "      <td>0.101581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.073598</td>\n",
       "      <td>0.146565</td>\n",
       "      <td>-0.045182</td>\n",
       "      <td>-0.212879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intensity_authority</th>\n",
       "      <td>-0.095532</td>\n",
       "      <td>-0.611298</td>\n",
       "      <td>-0.696074</td>\n",
       "      <td>-0.772854</td>\n",
       "      <td>-0.713299</td>\n",
       "      <td>-0.714851</td>\n",
       "      <td>0.657387</td>\n",
       "      <td>0.534478</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.784294</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.434173</td>\n",
       "      <td>-0.032953</td>\n",
       "      <td>0.004300</td>\n",
       "      <td>0.022012</td>\n",
       "      <td>-0.342573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.400997</td>\n",
       "      <td>0.369070</td>\n",
       "      <td>-0.398630</td>\n",
       "      <td>-0.556149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intensity_loyalty</th>\n",
       "      <td>-0.168525</td>\n",
       "      <td>-0.485312</td>\n",
       "      <td>-0.565156</td>\n",
       "      <td>-0.574256</td>\n",
       "      <td>-0.518633</td>\n",
       "      <td>-0.566234</td>\n",
       "      <td>0.550665</td>\n",
       "      <td>0.514202</td>\n",
       "      <td>0.784294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.439275</td>\n",
       "      <td>0.076268</td>\n",
       "      <td>-0.091916</td>\n",
       "      <td>-0.048548</td>\n",
       "      <td>-0.254642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.379563</td>\n",
       "      <td>0.263071</td>\n",
       "      <td>-0.490440</td>\n",
       "      <td>-0.375171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intensity_care</th>\n",
       "      <td>-0.277509</td>\n",
       "      <td>-0.382259</td>\n",
       "      <td>-0.333408</td>\n",
       "      <td>-0.451084</td>\n",
       "      <td>-0.446389</td>\n",
       "      <td>-0.363257</td>\n",
       "      <td>0.465903</td>\n",
       "      <td>0.814301</td>\n",
       "      <td>0.662621</td>\n",
       "      <td>0.702004</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.315906</td>\n",
       "      <td>0.256798</td>\n",
       "      <td>-0.114257</td>\n",
       "      <td>0.070324</td>\n",
       "      <td>0.020434</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.123554</td>\n",
       "      <td>0.126907</td>\n",
       "      <td>-0.248830</td>\n",
       "      <td>-0.172680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vice_sanctity</th>\n",
       "      <td>0.006378</td>\n",
       "      <td>-0.980723</td>\n",
       "      <td>-0.917999</td>\n",
       "      <td>-0.841521</td>\n",
       "      <td>-0.844217</td>\n",
       "      <td>-0.922378</td>\n",
       "      <td>0.908518</td>\n",
       "      <td>0.381996</td>\n",
       "      <td>0.629865</td>\n",
       "      <td>0.508126</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.585893</td>\n",
       "      <td>0.011979</td>\n",
       "      <td>-0.069890</td>\n",
       "      <td>0.010507</td>\n",
       "      <td>-0.566576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.515556</td>\n",
       "      <td>0.645316</td>\n",
       "      <td>-0.337405</td>\n",
       "      <td>-0.695942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virtue_sanctity</th>\n",
       "      <td>0.016753</td>\n",
       "      <td>0.969427</td>\n",
       "      <td>0.918972</td>\n",
       "      <td>0.849766</td>\n",
       "      <td>0.899590</td>\n",
       "      <td>0.916119</td>\n",
       "      <td>-0.777167</td>\n",
       "      <td>-0.426520</td>\n",
       "      <td>-0.622068</td>\n",
       "      <td>-0.474924</td>\n",
       "      <td>...</td>\n",
       "      <td>0.522197</td>\n",
       "      <td>-0.121778</td>\n",
       "      <td>0.024169</td>\n",
       "      <td>-0.061803</td>\n",
       "      <td>0.533781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.480788</td>\n",
       "      <td>-0.674955</td>\n",
       "      <td>0.259060</td>\n",
       "      <td>0.735233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vice_fairness</th>\n",
       "      <td>-0.111219</td>\n",
       "      <td>-0.890385</td>\n",
       "      <td>-0.967876</td>\n",
       "      <td>-0.897328</td>\n",
       "      <td>-0.900458</td>\n",
       "      <td>-0.957803</td>\n",
       "      <td>0.736956</td>\n",
       "      <td>0.487350</td>\n",
       "      <td>0.706804</td>\n",
       "      <td>0.560291</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.454489</td>\n",
       "      <td>0.109115</td>\n",
       "      <td>0.080370</td>\n",
       "      <td>0.182832</td>\n",
       "      <td>-0.437461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.379234</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>-0.234286</td>\n",
       "      <td>-0.729342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virtue_fairness</th>\n",
       "      <td>-0.018423</td>\n",
       "      <td>0.889307</td>\n",
       "      <td>0.978658</td>\n",
       "      <td>0.891016</td>\n",
       "      <td>0.848319</td>\n",
       "      <td>0.965193</td>\n",
       "      <td>-0.783400</td>\n",
       "      <td>-0.267581</td>\n",
       "      <td>-0.674640</td>\n",
       "      <td>-0.540869</td>\n",
       "      <td>...</td>\n",
       "      <td>0.540461</td>\n",
       "      <td>0.038752</td>\n",
       "      <td>-0.066011</td>\n",
       "      <td>-0.058529</td>\n",
       "      <td>0.522157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.490269</td>\n",
       "      <td>-0.623606</td>\n",
       "      <td>0.307977</td>\n",
       "      <td>0.730880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vice_authority</th>\n",
       "      <td>-0.166697</td>\n",
       "      <td>-0.777680</td>\n",
       "      <td>-0.863573</td>\n",
       "      <td>-0.970663</td>\n",
       "      <td>-0.905731</td>\n",
       "      <td>-0.890017</td>\n",
       "      <td>0.723556</td>\n",
       "      <td>0.493688</td>\n",
       "      <td>0.814827</td>\n",
       "      <td>0.607380</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.407151</td>\n",
       "      <td>0.091350</td>\n",
       "      <td>0.112797</td>\n",
       "      <td>0.204384</td>\n",
       "      <td>-0.374053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.323440</td>\n",
       "      <td>0.500447</td>\n",
       "      <td>-0.326531</td>\n",
       "      <td>-0.663831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virtue_authority</th>\n",
       "      <td>0.211511</td>\n",
       "      <td>0.794538</td>\n",
       "      <td>0.851002</td>\n",
       "      <td>0.948585</td>\n",
       "      <td>0.907545</td>\n",
       "      <td>0.880589</td>\n",
       "      <td>-0.690774</td>\n",
       "      <td>-0.473635</td>\n",
       "      <td>-0.663265</td>\n",
       "      <td>-0.536135</td>\n",
       "      <td>...</td>\n",
       "      <td>0.391831</td>\n",
       "      <td>-0.185238</td>\n",
       "      <td>-0.142436</td>\n",
       "      <td>-0.253208</td>\n",
       "      <td>0.334026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.280665</td>\n",
       "      <td>-0.493189</td>\n",
       "      <td>0.296208</td>\n",
       "      <td>0.614230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vice_loyalty</th>\n",
       "      <td>-0.241045</td>\n",
       "      <td>-0.814591</td>\n",
       "      <td>-0.848885</td>\n",
       "      <td>-0.886177</td>\n",
       "      <td>-0.971189</td>\n",
       "      <td>-0.875592</td>\n",
       "      <td>0.658492</td>\n",
       "      <td>0.587353</td>\n",
       "      <td>0.726199</td>\n",
       "      <td>0.604092</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.364927</td>\n",
       "      <td>0.230013</td>\n",
       "      <td>0.085946</td>\n",
       "      <td>0.275681</td>\n",
       "      <td>-0.379326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.281388</td>\n",
       "      <td>0.586906</td>\n",
       "      <td>-0.200308</td>\n",
       "      <td>-0.676823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virtue_loyalty</th>\n",
       "      <td>0.223162</td>\n",
       "      <td>0.856788</td>\n",
       "      <td>0.844020</td>\n",
       "      <td>0.885901</td>\n",
       "      <td>0.979723</td>\n",
       "      <td>0.876065</td>\n",
       "      <td>-0.677164</td>\n",
       "      <td>-0.550231</td>\n",
       "      <td>-0.668893</td>\n",
       "      <td>-0.461090</td>\n",
       "      <td>...</td>\n",
       "      <td>0.375355</td>\n",
       "      <td>-0.236443</td>\n",
       "      <td>-0.095966</td>\n",
       "      <td>-0.279836</td>\n",
       "      <td>0.412897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.305833</td>\n",
       "      <td>-0.574085</td>\n",
       "      <td>0.192970</td>\n",
       "      <td>0.683424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vice_care</th>\n",
       "      <td>-0.106012</td>\n",
       "      <td>-0.906493</td>\n",
       "      <td>-0.962011</td>\n",
       "      <td>-0.946836</td>\n",
       "      <td>-0.923127</td>\n",
       "      <td>-0.978948</td>\n",
       "      <td>0.811566</td>\n",
       "      <td>0.456225</td>\n",
       "      <td>0.741703</td>\n",
       "      <td>0.571692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.495753</td>\n",
       "      <td>0.075689</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>0.143186</td>\n",
       "      <td>-0.480631</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.439236</td>\n",
       "      <td>0.622133</td>\n",
       "      <td>-0.291500</td>\n",
       "      <td>-0.744162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virtue_care</th>\n",
       "      <td>0.055702</td>\n",
       "      <td>0.907821</td>\n",
       "      <td>0.977593</td>\n",
       "      <td>0.914462</td>\n",
       "      <td>0.898867</td>\n",
       "      <td>0.981393</td>\n",
       "      <td>-0.778166</td>\n",
       "      <td>-0.335630</td>\n",
       "      <td>-0.681214</td>\n",
       "      <td>-0.533610</td>\n",
       "      <td>...</td>\n",
       "      <td>0.515517</td>\n",
       "      <td>-0.031691</td>\n",
       "      <td>-0.058147</td>\n",
       "      <td>-0.120095</td>\n",
       "      <td>0.522907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.481656</td>\n",
       "      <td>-0.618175</td>\n",
       "      <td>0.303611</td>\n",
       "      <td>0.740927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anger</th>\n",
       "      <td>0.246884</td>\n",
       "      <td>0.429216</td>\n",
       "      <td>0.372502</td>\n",
       "      <td>0.393659</td>\n",
       "      <td>0.306543</td>\n",
       "      <td>0.389833</td>\n",
       "      <td>-0.530599</td>\n",
       "      <td>-0.314985</td>\n",
       "      <td>-0.279626</td>\n",
       "      <td>-0.324190</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355026</td>\n",
       "      <td>-0.087379</td>\n",
       "      <td>0.131562</td>\n",
       "      <td>-0.079897</td>\n",
       "      <td>0.088536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.121003</td>\n",
       "      <td>-0.145750</td>\n",
       "      <td>0.174745</td>\n",
       "      <td>0.100726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anticipation</th>\n",
       "      <td>-0.072007</td>\n",
       "      <td>0.565472</td>\n",
       "      <td>0.563723</td>\n",
       "      <td>0.511361</td>\n",
       "      <td>0.513794</td>\n",
       "      <td>0.566313</td>\n",
       "      <td>-0.520566</td>\n",
       "      <td>-0.141437</td>\n",
       "      <td>-0.423838</td>\n",
       "      <td>-0.403521</td>\n",
       "      <td>...</td>\n",
       "      <td>0.350857</td>\n",
       "      <td>0.020329</td>\n",
       "      <td>0.059554</td>\n",
       "      <td>0.073717</td>\n",
       "      <td>0.382259</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.396158</td>\n",
       "      <td>-0.525103</td>\n",
       "      <td>0.286438</td>\n",
       "      <td>0.572467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disgust</th>\n",
       "      <td>0.368544</td>\n",
       "      <td>0.211182</td>\n",
       "      <td>0.174942</td>\n",
       "      <td>0.192563</td>\n",
       "      <td>0.127262</td>\n",
       "      <td>0.188710</td>\n",
       "      <td>-0.271789</td>\n",
       "      <td>-0.303834</td>\n",
       "      <td>-0.108747</td>\n",
       "      <td>-0.214207</td>\n",
       "      <td>...</td>\n",
       "      <td>0.139004</td>\n",
       "      <td>-0.182332</td>\n",
       "      <td>0.028508</td>\n",
       "      <td>-0.182319</td>\n",
       "      <td>-0.113468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.102672</td>\n",
       "      <td>-0.004681</td>\n",
       "      <td>-0.012926</td>\n",
       "      <td>-0.104289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fear</th>\n",
       "      <td>0.265372</td>\n",
       "      <td>-0.757140</td>\n",
       "      <td>-0.736075</td>\n",
       "      <td>-0.688434</td>\n",
       "      <td>-0.663423</td>\n",
       "      <td>-0.743609</td>\n",
       "      <td>0.786661</td>\n",
       "      <td>0.166697</td>\n",
       "      <td>0.672509</td>\n",
       "      <td>0.583434</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.730092</td>\n",
       "      <td>-0.202241</td>\n",
       "      <td>-0.222912</td>\n",
       "      <td>-0.310449</td>\n",
       "      <td>-0.622041</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.756667</td>\n",
       "      <td>0.613507</td>\n",
       "      <td>-0.416040</td>\n",
       "      <td>-0.638399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>joy</th>\n",
       "      <td>-0.305689</td>\n",
       "      <td>0.782519</td>\n",
       "      <td>0.764004</td>\n",
       "      <td>0.699598</td>\n",
       "      <td>0.693733</td>\n",
       "      <td>0.758731</td>\n",
       "      <td>-0.761164</td>\n",
       "      <td>-0.193286</td>\n",
       "      <td>-0.646276</td>\n",
       "      <td>-0.471097</td>\n",
       "      <td>...</td>\n",
       "      <td>0.677546</td>\n",
       "      <td>0.209894</td>\n",
       "      <td>0.165777</td>\n",
       "      <td>0.314117</td>\n",
       "      <td>0.613626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.713076</td>\n",
       "      <td>-0.613652</td>\n",
       "      <td>0.349332</td>\n",
       "      <td>0.723359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>love</th>\n",
       "      <td>-0.266621</td>\n",
       "      <td>0.738770</td>\n",
       "      <td>0.708237</td>\n",
       "      <td>0.656388</td>\n",
       "      <td>0.672667</td>\n",
       "      <td>0.711879</td>\n",
       "      <td>-0.667749</td>\n",
       "      <td>-0.131693</td>\n",
       "      <td>-0.482787</td>\n",
       "      <td>-0.260888</td>\n",
       "      <td>...</td>\n",
       "      <td>0.545563</td>\n",
       "      <td>0.174048</td>\n",
       "      <td>0.132956</td>\n",
       "      <td>0.166040</td>\n",
       "      <td>0.552940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.561422</td>\n",
       "      <td>-0.633587</td>\n",
       "      <td>0.173009</td>\n",
       "      <td>0.661372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>optimism</th>\n",
       "      <td>-0.125079</td>\n",
       "      <td>0.850778</td>\n",
       "      <td>0.839102</td>\n",
       "      <td>0.787424</td>\n",
       "      <td>0.776339</td>\n",
       "      <td>0.841192</td>\n",
       "      <td>-0.780191</td>\n",
       "      <td>-0.255891</td>\n",
       "      <td>-0.659071</td>\n",
       "      <td>-0.542907</td>\n",
       "      <td>...</td>\n",
       "      <td>0.609786</td>\n",
       "      <td>0.081357</td>\n",
       "      <td>0.082198</td>\n",
       "      <td>0.130523</td>\n",
       "      <td>0.660491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.651812</td>\n",
       "      <td>-0.601594</td>\n",
       "      <td>0.446021</td>\n",
       "      <td>0.811027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pessimism</th>\n",
       "      <td>-0.260585</td>\n",
       "      <td>0.702543</td>\n",
       "      <td>0.597268</td>\n",
       "      <td>0.543473</td>\n",
       "      <td>0.559528</td>\n",
       "      <td>0.602133</td>\n",
       "      <td>-0.624855</td>\n",
       "      <td>-0.012466</td>\n",
       "      <td>-0.329739</td>\n",
       "      <td>-0.209710</td>\n",
       "      <td>...</td>\n",
       "      <td>0.622120</td>\n",
       "      <td>0.152535</td>\n",
       "      <td>0.162332</td>\n",
       "      <td>0.305320</td>\n",
       "      <td>0.548193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.660885</td>\n",
       "      <td>-0.404902</td>\n",
       "      <td>0.287371</td>\n",
       "      <td>0.484023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sadness</th>\n",
       "      <td>0.179163</td>\n",
       "      <td>0.299127</td>\n",
       "      <td>0.275260</td>\n",
       "      <td>0.281796</td>\n",
       "      <td>0.370214</td>\n",
       "      <td>0.278456</td>\n",
       "      <td>-0.161635</td>\n",
       "      <td>-0.078293</td>\n",
       "      <td>-0.215864</td>\n",
       "      <td>-0.160267</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130234</td>\n",
       "      <td>-0.014504</td>\n",
       "      <td>-0.025208</td>\n",
       "      <td>-0.032348</td>\n",
       "      <td>0.305794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.345874</td>\n",
       "      <td>-0.265464</td>\n",
       "      <td>0.265161</td>\n",
       "      <td>0.280546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surprise</th>\n",
       "      <td>0.145264</td>\n",
       "      <td>-0.240979</td>\n",
       "      <td>-0.297352</td>\n",
       "      <td>-0.194732</td>\n",
       "      <td>-0.154613</td>\n",
       "      <td>-0.252696</td>\n",
       "      <td>0.204266</td>\n",
       "      <td>0.030126</td>\n",
       "      <td>0.172812</td>\n",
       "      <td>0.215745</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.234207</td>\n",
       "      <td>-0.094743</td>\n",
       "      <td>-0.047694</td>\n",
       "      <td>-0.032506</td>\n",
       "      <td>-0.091902</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.216271</td>\n",
       "      <td>0.189380</td>\n",
       "      <td>-0.170879</td>\n",
       "      <td>-0.168749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trust</th>\n",
       "      <td>0.146171</td>\n",
       "      <td>0.551415</td>\n",
       "      <td>0.556491</td>\n",
       "      <td>0.578924</td>\n",
       "      <td>0.585433</td>\n",
       "      <td>0.586892</td>\n",
       "      <td>-0.503327</td>\n",
       "      <td>-0.299679</td>\n",
       "      <td>-0.410662</td>\n",
       "      <td>-0.300376</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218572</td>\n",
       "      <td>-0.181030</td>\n",
       "      <td>-0.142055</td>\n",
       "      <td>-0.258297</td>\n",
       "      <td>0.430781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.271171</td>\n",
       "      <td>-0.292526</td>\n",
       "      <td>0.337405</td>\n",
       "      <td>0.538278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anger_emolex</th>\n",
       "      <td>-0.274379</td>\n",
       "      <td>0.590114</td>\n",
       "      <td>0.550652</td>\n",
       "      <td>0.476331</td>\n",
       "      <td>0.407782</td>\n",
       "      <td>0.554558</td>\n",
       "      <td>-0.655218</td>\n",
       "      <td>-0.118714</td>\n",
       "      <td>-0.434173</td>\n",
       "      <td>-0.439275</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.217310</td>\n",
       "      <td>0.456067</td>\n",
       "      <td>0.548732</td>\n",
       "      <td>0.494201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.773985</td>\n",
       "      <td>-0.274485</td>\n",
       "      <td>0.412069</td>\n",
       "      <td>0.378971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anticipation_emolex</th>\n",
       "      <td>-0.349897</td>\n",
       "      <td>-0.061843</td>\n",
       "      <td>0.007693</td>\n",
       "      <td>-0.060699</td>\n",
       "      <td>-0.203398</td>\n",
       "      <td>-0.018462</td>\n",
       "      <td>-0.136033</td>\n",
       "      <td>0.451925</td>\n",
       "      <td>-0.032953</td>\n",
       "      <td>0.076268</td>\n",
       "      <td>...</td>\n",
       "      <td>0.217310</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051257</td>\n",
       "      <td>0.388426</td>\n",
       "      <td>0.426691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.319219</td>\n",
       "      <td>0.073625</td>\n",
       "      <td>0.265950</td>\n",
       "      <td>0.081462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disgust_emolex</th>\n",
       "      <td>-0.192812</td>\n",
       "      <td>0.077859</td>\n",
       "      <td>-0.020316</td>\n",
       "      <td>-0.057082</td>\n",
       "      <td>-0.072467</td>\n",
       "      <td>-0.017029</td>\n",
       "      <td>-0.157677</td>\n",
       "      <td>-0.012900</td>\n",
       "      <td>0.004300</td>\n",
       "      <td>-0.091916</td>\n",
       "      <td>...</td>\n",
       "      <td>0.456067</td>\n",
       "      <td>0.051257</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.473793</td>\n",
       "      <td>0.163896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.391713</td>\n",
       "      <td>0.065104</td>\n",
       "      <td>0.206975</td>\n",
       "      <td>0.012676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fear_emolex</th>\n",
       "      <td>-0.481656</td>\n",
       "      <td>-0.018212</td>\n",
       "      <td>-0.072020</td>\n",
       "      <td>-0.125618</td>\n",
       "      <td>-0.228606</td>\n",
       "      <td>-0.097623</td>\n",
       "      <td>-0.115874</td>\n",
       "      <td>0.311290</td>\n",
       "      <td>0.022012</td>\n",
       "      <td>-0.048548</td>\n",
       "      <td>...</td>\n",
       "      <td>0.548732</td>\n",
       "      <td>0.388426</td>\n",
       "      <td>0.473793</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.277167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.592784</td>\n",
       "      <td>0.122410</td>\n",
       "      <td>0.275642</td>\n",
       "      <td>-0.010704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>joy_emolex</th>\n",
       "      <td>-0.143620</td>\n",
       "      <td>0.559410</td>\n",
       "      <td>0.540777</td>\n",
       "      <td>0.461353</td>\n",
       "      <td>0.418972</td>\n",
       "      <td>0.543407</td>\n",
       "      <td>-0.559015</td>\n",
       "      <td>0.101581</td>\n",
       "      <td>-0.342573</td>\n",
       "      <td>-0.254642</td>\n",
       "      <td>...</td>\n",
       "      <td>0.494201</td>\n",
       "      <td>0.426691</td>\n",
       "      <td>0.163896</td>\n",
       "      <td>0.277167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.728671</td>\n",
       "      <td>-0.319417</td>\n",
       "      <td>0.617373</td>\n",
       "      <td>0.639662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>love_emolex</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sadness_emolex</th>\n",
       "      <td>-0.349306</td>\n",
       "      <td>0.512860</td>\n",
       "      <td>0.499632</td>\n",
       "      <td>0.413423</td>\n",
       "      <td>0.343349</td>\n",
       "      <td>0.495805</td>\n",
       "      <td>-0.571284</td>\n",
       "      <td>0.073598</td>\n",
       "      <td>-0.400997</td>\n",
       "      <td>-0.379563</td>\n",
       "      <td>...</td>\n",
       "      <td>0.773985</td>\n",
       "      <td>0.319219</td>\n",
       "      <td>0.391713</td>\n",
       "      <td>0.592784</td>\n",
       "      <td>0.728671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.293459</td>\n",
       "      <td>0.576965</td>\n",
       "      <td>0.479066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surprise_emolex</th>\n",
       "      <td>0.125842</td>\n",
       "      <td>-0.668394</td>\n",
       "      <td>-0.671076</td>\n",
       "      <td>-0.529495</td>\n",
       "      <td>-0.636309</td>\n",
       "      <td>-0.647920</td>\n",
       "      <td>0.524708</td>\n",
       "      <td>0.146565</td>\n",
       "      <td>0.369070</td>\n",
       "      <td>0.263071</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.274485</td>\n",
       "      <td>0.073625</td>\n",
       "      <td>0.065104</td>\n",
       "      <td>0.122410</td>\n",
       "      <td>-0.319417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.293459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.088168</td>\n",
       "      <td>-0.557227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trust_emolex</th>\n",
       "      <td>0.078345</td>\n",
       "      <td>0.286372</td>\n",
       "      <td>0.297457</td>\n",
       "      <td>0.350476</td>\n",
       "      <td>0.194851</td>\n",
       "      <td>0.321231</td>\n",
       "      <td>-0.459289</td>\n",
       "      <td>-0.045182</td>\n",
       "      <td>-0.398630</td>\n",
       "      <td>-0.490440</td>\n",
       "      <td>...</td>\n",
       "      <td>0.412069</td>\n",
       "      <td>0.265950</td>\n",
       "      <td>0.206975</td>\n",
       "      <td>0.275642</td>\n",
       "      <td>0.617373</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.576965</td>\n",
       "      <td>0.088168</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.346084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vader_sent</th>\n",
       "      <td>-0.129957</td>\n",
       "      <td>0.705147</td>\n",
       "      <td>0.761835</td>\n",
       "      <td>0.686777</td>\n",
       "      <td>0.697914</td>\n",
       "      <td>0.753813</td>\n",
       "      <td>-0.584710</td>\n",
       "      <td>-0.212879</td>\n",
       "      <td>-0.556149</td>\n",
       "      <td>-0.375171</td>\n",
       "      <td>...</td>\n",
       "      <td>0.378971</td>\n",
       "      <td>0.081462</td>\n",
       "      <td>0.012676</td>\n",
       "      <td>-0.010704</td>\n",
       "      <td>0.639662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.479066</td>\n",
       "      <td>-0.557227</td>\n",
       "      <td>0.346084</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>42 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     num_tot_tweets  bias_sanctity  bias_fairness  \\\n",
       "num_tot_tweets             1.000000      -0.006049      -0.005838   \n",
       "bias_sanctity             -0.006049       1.000000       0.925981   \n",
       "bias_fairness             -0.005838       0.925981       1.000000   \n",
       "bias_authority             0.142857       0.842073       0.900813   \n",
       "bias_loyalty               0.179637       0.872765       0.882219   \n",
       "bias_care                  0.055360       0.927782       0.988060   \n",
       "intensity_sanctity         0.046221      -0.858747      -0.811001   \n",
       "intensity_fairness        -0.428992      -0.404705      -0.360983   \n",
       "intensity_authority       -0.095532      -0.611298      -0.696074   \n",
       "intensity_loyalty         -0.168525      -0.485312      -0.565156   \n",
       "intensity_care            -0.277509      -0.382259      -0.333408   \n",
       "vice_sanctity              0.006378      -0.980723      -0.917999   \n",
       "virtue_sanctity            0.016753       0.969427       0.918972   \n",
       "vice_fairness             -0.111219      -0.890385      -0.967876   \n",
       "virtue_fairness           -0.018423       0.889307       0.978658   \n",
       "vice_authority            -0.166697      -0.777680      -0.863573   \n",
       "virtue_authority           0.211511       0.794538       0.851002   \n",
       "vice_loyalty              -0.241045      -0.814591      -0.848885   \n",
       "virtue_loyalty             0.223162       0.856788       0.844020   \n",
       "vice_care                 -0.106012      -0.906493      -0.962011   \n",
       "virtue_care                0.055702       0.907821       0.977593   \n",
       "anger                      0.246884       0.429216       0.372502   \n",
       "anticipation              -0.072007       0.565472       0.563723   \n",
       "disgust                    0.368544       0.211182       0.174942   \n",
       "fear                       0.265372      -0.757140      -0.736075   \n",
       "joy                       -0.305689       0.782519       0.764004   \n",
       "love                      -0.266621       0.738770       0.708237   \n",
       "optimism                  -0.125079       0.850778       0.839102   \n",
       "pessimism                 -0.260585       0.702543       0.597268   \n",
       "sadness                    0.179163       0.299127       0.275260   \n",
       "surprise                   0.145264      -0.240979      -0.297352   \n",
       "trust                      0.146171       0.551415       0.556491   \n",
       "anger_emolex              -0.274379       0.590114       0.550652   \n",
       "anticipation_emolex       -0.349897      -0.061843       0.007693   \n",
       "disgust_emolex            -0.192812       0.077859      -0.020316   \n",
       "fear_emolex               -0.481656      -0.018212      -0.072020   \n",
       "joy_emolex                -0.143620       0.559410       0.540777   \n",
       "love_emolex                     NaN            NaN            NaN   \n",
       "sadness_emolex            -0.349306       0.512860       0.499632   \n",
       "surprise_emolex            0.125842      -0.668394      -0.671076   \n",
       "trust_emolex               0.078345       0.286372       0.297457   \n",
       "vader_sent                -0.129957       0.705147       0.761835   \n",
       "\n",
       "                     bias_authority  bias_loyalty  bias_care  \\\n",
       "num_tot_tweets             0.142857      0.179637   0.055360   \n",
       "bias_sanctity              0.842073      0.872765   0.927782   \n",
       "bias_fairness              0.900813      0.882219   0.988060   \n",
       "bias_authority             1.000000      0.923969   0.934134   \n",
       "bias_loyalty               0.923969      1.000000   0.911793   \n",
       "bias_care                  0.934134      0.911793   1.000000   \n",
       "intensity_sanctity        -0.779521     -0.707264  -0.830160   \n",
       "intensity_fairness        -0.431964     -0.524182  -0.376762   \n",
       "intensity_authority       -0.772854     -0.713299  -0.714851   \n",
       "intensity_loyalty         -0.574256     -0.518633  -0.566234   \n",
       "intensity_care            -0.451084     -0.446389  -0.363257   \n",
       "vice_sanctity             -0.841521     -0.844217  -0.922378   \n",
       "virtue_sanctity            0.849766      0.899590   0.916119   \n",
       "vice_fairness             -0.897328     -0.900458  -0.957803   \n",
       "virtue_fairness            0.891016      0.848319   0.965193   \n",
       "vice_authority            -0.970663     -0.905731  -0.890017   \n",
       "virtue_authority           0.948585      0.907545   0.880589   \n",
       "vice_loyalty              -0.886177     -0.971189  -0.875592   \n",
       "virtue_loyalty             0.885901      0.979723   0.876065   \n",
       "vice_care                 -0.946836     -0.923127  -0.978948   \n",
       "virtue_care                0.914462      0.898867   0.981393   \n",
       "anger                      0.393659      0.306543   0.389833   \n",
       "anticipation               0.511361      0.513794   0.566313   \n",
       "disgust                    0.192563      0.127262   0.188710   \n",
       "fear                      -0.688434     -0.663423  -0.743609   \n",
       "joy                        0.699598      0.693733   0.758731   \n",
       "love                       0.656388      0.672667   0.711879   \n",
       "optimism                   0.787424      0.776339   0.841192   \n",
       "pessimism                  0.543473      0.559528   0.602133   \n",
       "sadness                    0.281796      0.370214   0.278456   \n",
       "surprise                  -0.194732     -0.154613  -0.252696   \n",
       "trust                      0.578924      0.585433   0.586892   \n",
       "anger_emolex               0.476331      0.407782   0.554558   \n",
       "anticipation_emolex       -0.060699     -0.203398  -0.018462   \n",
       "disgust_emolex            -0.057082     -0.072467  -0.017029   \n",
       "fear_emolex               -0.125618     -0.228606  -0.097623   \n",
       "joy_emolex                 0.461353      0.418972   0.543407   \n",
       "love_emolex                     NaN           NaN        NaN   \n",
       "sadness_emolex             0.413423      0.343349   0.495805   \n",
       "surprise_emolex           -0.529495     -0.636309  -0.647920   \n",
       "trust_emolex               0.350476      0.194851   0.321231   \n",
       "vader_sent                 0.686777      0.697914   0.753813   \n",
       "\n",
       "                     intensity_sanctity  intensity_fairness  \\\n",
       "num_tot_tweets                 0.046221           -0.428992   \n",
       "bias_sanctity                 -0.858747           -0.404705   \n",
       "bias_fairness                 -0.811001           -0.360983   \n",
       "bias_authority                -0.779521           -0.431964   \n",
       "bias_loyalty                  -0.707264           -0.524182   \n",
       "bias_care                     -0.830160           -0.376762   \n",
       "intensity_sanctity             1.000000            0.310541   \n",
       "intensity_fairness             0.310541            1.000000   \n",
       "intensity_authority            0.657387            0.534478   \n",
       "intensity_loyalty              0.550665            0.514202   \n",
       "intensity_care                 0.465903            0.814301   \n",
       "vice_sanctity                  0.908518            0.381996   \n",
       "virtue_sanctity               -0.777167           -0.426520   \n",
       "vice_fairness                  0.736956            0.487350   \n",
       "virtue_fairness               -0.783400           -0.267581   \n",
       "vice_authority                 0.723556            0.493688   \n",
       "virtue_authority              -0.690774           -0.473635   \n",
       "vice_loyalty                   0.658492            0.587353   \n",
       "virtue_loyalty                -0.677164           -0.550231   \n",
       "vice_care                      0.811566            0.456225   \n",
       "virtue_care                   -0.778166           -0.335630   \n",
       "anger                         -0.530599           -0.314985   \n",
       "anticipation                  -0.520566           -0.141437   \n",
       "disgust                       -0.271789           -0.303834   \n",
       "fear                           0.786661            0.166697   \n",
       "joy                           -0.761164           -0.193286   \n",
       "love                          -0.667749           -0.131693   \n",
       "optimism                      -0.780191           -0.255891   \n",
       "pessimism                     -0.624855           -0.012466   \n",
       "sadness                       -0.161635           -0.078293   \n",
       "surprise                       0.204266            0.030126   \n",
       "trust                         -0.503327           -0.299679   \n",
       "anger_emolex                  -0.655218           -0.118714   \n",
       "anticipation_emolex           -0.136033            0.451925   \n",
       "disgust_emolex                -0.157677           -0.012900   \n",
       "fear_emolex                   -0.115874            0.311290   \n",
       "joy_emolex                    -0.559015            0.101581   \n",
       "love_emolex                         NaN                 NaN   \n",
       "sadness_emolex                -0.571284            0.073598   \n",
       "surprise_emolex                0.524708            0.146565   \n",
       "trust_emolex                  -0.459289           -0.045182   \n",
       "vader_sent                    -0.584710           -0.212879   \n",
       "\n",
       "                     intensity_authority  intensity_loyalty  ...  \\\n",
       "num_tot_tweets                 -0.095532          -0.168525  ...   \n",
       "bias_sanctity                  -0.611298          -0.485312  ...   \n",
       "bias_fairness                  -0.696074          -0.565156  ...   \n",
       "bias_authority                 -0.772854          -0.574256  ...   \n",
       "bias_loyalty                   -0.713299          -0.518633  ...   \n",
       "bias_care                      -0.714851          -0.566234  ...   \n",
       "intensity_sanctity              0.657387           0.550665  ...   \n",
       "intensity_fairness              0.534478           0.514202  ...   \n",
       "intensity_authority             1.000000           0.784294  ...   \n",
       "intensity_loyalty               0.784294           1.000000  ...   \n",
       "intensity_care                  0.662621           0.702004  ...   \n",
       "vice_sanctity                   0.629865           0.508126  ...   \n",
       "virtue_sanctity                -0.622068          -0.474924  ...   \n",
       "vice_fairness                   0.706804           0.560291  ...   \n",
       "virtue_fairness                -0.674640          -0.540869  ...   \n",
       "vice_authority                  0.814827           0.607380  ...   \n",
       "virtue_authority               -0.663265          -0.536135  ...   \n",
       "vice_loyalty                    0.726199           0.604092  ...   \n",
       "virtue_loyalty                 -0.668893          -0.461090  ...   \n",
       "vice_care                       0.741703           0.571692  ...   \n",
       "virtue_care                    -0.681214          -0.533610  ...   \n",
       "anger                          -0.279626          -0.324190  ...   \n",
       "anticipation                   -0.423838          -0.403521  ...   \n",
       "disgust                        -0.108747          -0.214207  ...   \n",
       "fear                            0.672509           0.583434  ...   \n",
       "joy                            -0.646276          -0.471097  ...   \n",
       "love                           -0.482787          -0.260888  ...   \n",
       "optimism                       -0.659071          -0.542907  ...   \n",
       "pessimism                      -0.329739          -0.209710  ...   \n",
       "sadness                        -0.215864          -0.160267  ...   \n",
       "surprise                        0.172812           0.215745  ...   \n",
       "trust                          -0.410662          -0.300376  ...   \n",
       "anger_emolex                   -0.434173          -0.439275  ...   \n",
       "anticipation_emolex            -0.032953           0.076268  ...   \n",
       "disgust_emolex                  0.004300          -0.091916  ...   \n",
       "fear_emolex                     0.022012          -0.048548  ...   \n",
       "joy_emolex                     -0.342573          -0.254642  ...   \n",
       "love_emolex                          NaN                NaN  ...   \n",
       "sadness_emolex                 -0.400997          -0.379563  ...   \n",
       "surprise_emolex                 0.369070           0.263071  ...   \n",
       "trust_emolex                   -0.398630          -0.490440  ...   \n",
       "vader_sent                     -0.556149          -0.375171  ...   \n",
       "\n",
       "                     anger_emolex  anticipation_emolex  disgust_emolex  \\\n",
       "num_tot_tweets          -0.274379            -0.349897       -0.192812   \n",
       "bias_sanctity            0.590114            -0.061843        0.077859   \n",
       "bias_fairness            0.550652             0.007693       -0.020316   \n",
       "bias_authority           0.476331            -0.060699       -0.057082   \n",
       "bias_loyalty             0.407782            -0.203398       -0.072467   \n",
       "bias_care                0.554558            -0.018462       -0.017029   \n",
       "intensity_sanctity      -0.655218            -0.136033       -0.157677   \n",
       "intensity_fairness      -0.118714             0.451925       -0.012900   \n",
       "intensity_authority     -0.434173            -0.032953        0.004300   \n",
       "intensity_loyalty       -0.439275             0.076268       -0.091916   \n",
       "intensity_care          -0.315906             0.256798       -0.114257   \n",
       "vice_sanctity           -0.585893             0.011979       -0.069890   \n",
       "virtue_sanctity          0.522197            -0.121778        0.024169   \n",
       "vice_fairness           -0.454489             0.109115        0.080370   \n",
       "virtue_fairness          0.540461             0.038752       -0.066011   \n",
       "vice_authority          -0.407151             0.091350        0.112797   \n",
       "virtue_authority         0.391831            -0.185238       -0.142436   \n",
       "vice_loyalty            -0.364927             0.230013        0.085946   \n",
       "virtue_loyalty           0.375355            -0.236443       -0.095966   \n",
       "vice_care               -0.495753             0.075689        0.025826   \n",
       "virtue_care              0.515517            -0.031691       -0.058147   \n",
       "anger                    0.355026            -0.087379        0.131562   \n",
       "anticipation             0.350857             0.020329        0.059554   \n",
       "disgust                  0.139004            -0.182332        0.028508   \n",
       "fear                    -0.730092            -0.202241       -0.222912   \n",
       "joy                      0.677546             0.209894        0.165777   \n",
       "love                     0.545563             0.174048        0.132956   \n",
       "optimism                 0.609786             0.081357        0.082198   \n",
       "pessimism                0.622120             0.152535        0.162332   \n",
       "sadness                  0.130234            -0.014504       -0.025208   \n",
       "surprise                -0.234207            -0.094743       -0.047694   \n",
       "trust                    0.218572            -0.181030       -0.142055   \n",
       "anger_emolex             1.000000             0.217310        0.456067   \n",
       "anticipation_emolex      0.217310             1.000000        0.051257   \n",
       "disgust_emolex           0.456067             0.051257        1.000000   \n",
       "fear_emolex              0.548732             0.388426        0.473793   \n",
       "joy_emolex               0.494201             0.426691        0.163896   \n",
       "love_emolex                   NaN                  NaN             NaN   \n",
       "sadness_emolex           0.773985             0.319219        0.391713   \n",
       "surprise_emolex         -0.274485             0.073625        0.065104   \n",
       "trust_emolex             0.412069             0.265950        0.206975   \n",
       "vader_sent               0.378971             0.081462        0.012676   \n",
       "\n",
       "                     fear_emolex  joy_emolex  love_emolex  sadness_emolex  \\\n",
       "num_tot_tweets         -0.481656   -0.143620          NaN       -0.349306   \n",
       "bias_sanctity          -0.018212    0.559410          NaN        0.512860   \n",
       "bias_fairness          -0.072020    0.540777          NaN        0.499632   \n",
       "bias_authority         -0.125618    0.461353          NaN        0.413423   \n",
       "bias_loyalty           -0.228606    0.418972          NaN        0.343349   \n",
       "bias_care              -0.097623    0.543407          NaN        0.495805   \n",
       "intensity_sanctity     -0.115874   -0.559015          NaN       -0.571284   \n",
       "intensity_fairness      0.311290    0.101581          NaN        0.073598   \n",
       "intensity_authority     0.022012   -0.342573          NaN       -0.400997   \n",
       "intensity_loyalty      -0.048548   -0.254642          NaN       -0.379563   \n",
       "intensity_care          0.070324    0.020434          NaN       -0.123554   \n",
       "vice_sanctity           0.010507   -0.566576          NaN       -0.515556   \n",
       "virtue_sanctity        -0.061803    0.533781          NaN        0.480788   \n",
       "vice_fairness           0.182832   -0.437461          NaN       -0.379234   \n",
       "virtue_fairness        -0.058529    0.522157          NaN        0.490269   \n",
       "vice_authority          0.204384   -0.374053          NaN       -0.323440   \n",
       "virtue_authority       -0.253208    0.334026          NaN        0.280665   \n",
       "vice_loyalty            0.275681   -0.379326          NaN       -0.281388   \n",
       "virtue_loyalty         -0.279836    0.412897          NaN        0.305833   \n",
       "vice_care               0.143186   -0.480631          NaN       -0.439236   \n",
       "virtue_care            -0.120095    0.522907          NaN        0.481656   \n",
       "anger                  -0.079897    0.088536          NaN        0.121003   \n",
       "anticipation            0.073717    0.382259          NaN        0.396158   \n",
       "disgust                -0.182319   -0.113468          NaN       -0.102672   \n",
       "fear                   -0.310449   -0.622041          NaN       -0.756667   \n",
       "joy                     0.314117    0.613626          NaN        0.713076   \n",
       "love                    0.166040    0.552940          NaN        0.561422   \n",
       "optimism                0.130523    0.660491          NaN        0.651812   \n",
       "pessimism               0.305320    0.548193          NaN        0.660885   \n",
       "sadness                -0.032348    0.305794          NaN        0.345874   \n",
       "surprise               -0.032506   -0.091902          NaN       -0.216271   \n",
       "trust                  -0.258297    0.430781          NaN        0.271171   \n",
       "anger_emolex            0.548732    0.494201          NaN        0.773985   \n",
       "anticipation_emolex     0.388426    0.426691          NaN        0.319219   \n",
       "disgust_emolex          0.473793    0.163896          NaN        0.391713   \n",
       "fear_emolex             1.000000    0.277167          NaN        0.592784   \n",
       "joy_emolex              0.277167    1.000000          NaN        0.728671   \n",
       "love_emolex                  NaN         NaN          NaN             NaN   \n",
       "sadness_emolex          0.592784    0.728671          NaN        1.000000   \n",
       "surprise_emolex         0.122410   -0.319417          NaN       -0.293459   \n",
       "trust_emolex            0.275642    0.617373          NaN        0.576965   \n",
       "vader_sent             -0.010704    0.639662          NaN        0.479066   \n",
       "\n",
       "                     surprise_emolex  trust_emolex  vader_sent  \n",
       "num_tot_tweets              0.125842      0.078345   -0.129957  \n",
       "bias_sanctity              -0.668394      0.286372    0.705147  \n",
       "bias_fairness              -0.671076      0.297457    0.761835  \n",
       "bias_authority             -0.529495      0.350476    0.686777  \n",
       "bias_loyalty               -0.636309      0.194851    0.697914  \n",
       "bias_care                  -0.647920      0.321231    0.753813  \n",
       "intensity_sanctity          0.524708     -0.459289   -0.584710  \n",
       "intensity_fairness          0.146565     -0.045182   -0.212879  \n",
       "intensity_authority         0.369070     -0.398630   -0.556149  \n",
       "intensity_loyalty           0.263071     -0.490440   -0.375171  \n",
       "intensity_care              0.126907     -0.248830   -0.172680  \n",
       "vice_sanctity               0.645316     -0.337405   -0.695942  \n",
       "virtue_sanctity            -0.674955      0.259060    0.735233  \n",
       "vice_fairness               0.625000     -0.234286   -0.729342  \n",
       "virtue_fairness            -0.623606      0.307977    0.730880  \n",
       "vice_authority              0.500447     -0.326531   -0.663831  \n",
       "virtue_authority           -0.493189      0.296208    0.614230  \n",
       "vice_loyalty                0.586906     -0.200308   -0.676823  \n",
       "virtue_loyalty             -0.574085      0.192970    0.683424  \n",
       "vice_care                   0.622133     -0.291500   -0.744162  \n",
       "virtue_care                -0.618175      0.303611    0.740927  \n",
       "anger                      -0.145750      0.174745    0.100726  \n",
       "anticipation               -0.525103      0.286438    0.572467  \n",
       "disgust                    -0.004681     -0.012926   -0.104289  \n",
       "fear                        0.613507     -0.416040   -0.638399  \n",
       "joy                        -0.613652      0.349332    0.723359  \n",
       "love                       -0.633587      0.173009    0.661372  \n",
       "optimism                   -0.601594      0.446021    0.811027  \n",
       "pessimism                  -0.404902      0.287371    0.484023  \n",
       "sadness                    -0.265464      0.265161    0.280546  \n",
       "surprise                    0.189380     -0.170879   -0.168749  \n",
       "trust                      -0.292526      0.337405    0.538278  \n",
       "anger_emolex               -0.274485      0.412069    0.378971  \n",
       "anticipation_emolex         0.073625      0.265950    0.081462  \n",
       "disgust_emolex              0.065104      0.206975    0.012676  \n",
       "fear_emolex                 0.122410      0.275642   -0.010704  \n",
       "joy_emolex                 -0.319417      0.617373    0.639662  \n",
       "love_emolex                      NaN           NaN         NaN  \n",
       "sadness_emolex             -0.293459      0.576965    0.479066  \n",
       "surprise_emolex             1.000000      0.088168   -0.557227  \n",
       "trust_emolex                0.088168      1.000000    0.346084  \n",
       "vader_sent                 -0.557227      0.346084    1.000000  \n",
       "\n",
       "[42 rows x 42 columns]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr #.to_csv('corr_matrix_between_all_vars.csv')\n",
    "\n",
    "# EmoLex didn't label any tweet with love"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "4b4679c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "emolex_cols = ['anger_emolex', 'anticipation_emolex', 'disgust_emolex', 'fear_emolex',\n",
    "       'joy_emolex', 'love_emolex', 'sadness_emolex', 'surprise_emolex','trust_emolex']\n",
    "spanemo_cols = ['anger', 'anticipation', 'disgust', 'fear',\n",
    "       'joy', 'love', 'optimism', 'pessimism', 'sadness', 'surprise', 'trust']\n",
    "mf_bias = ['bias_sanctity', 'bias_fairness', 'bias_authority', 'bias_loyalty', 'bias_care']\n",
    "mf_intensity = ['intensity_sanctity', 'intensity_fairness', 'intensity_authority', 'intensity_loyalty', 'intensity_care']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "5debf8cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corrlation b/t MF bias and Emolex\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bias_sanctity</th>\n",
       "      <th>bias_fairness</th>\n",
       "      <th>bias_authority</th>\n",
       "      <th>bias_loyalty</th>\n",
       "      <th>bias_care</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>anger_emolex</th>\n",
       "      <td>0.590114</td>\n",
       "      <td>0.550652</td>\n",
       "      <td>0.476331</td>\n",
       "      <td>0.407782</td>\n",
       "      <td>0.554558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anticipation_emolex</th>\n",
       "      <td>-0.061843</td>\n",
       "      <td>0.007693</td>\n",
       "      <td>-0.060699</td>\n",
       "      <td>-0.203398</td>\n",
       "      <td>-0.018462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disgust_emolex</th>\n",
       "      <td>0.077859</td>\n",
       "      <td>-0.020316</td>\n",
       "      <td>-0.057082</td>\n",
       "      <td>-0.072467</td>\n",
       "      <td>-0.017029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fear_emolex</th>\n",
       "      <td>-0.018212</td>\n",
       "      <td>-0.072020</td>\n",
       "      <td>-0.125618</td>\n",
       "      <td>-0.228606</td>\n",
       "      <td>-0.097623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>joy_emolex</th>\n",
       "      <td>0.559410</td>\n",
       "      <td>0.540777</td>\n",
       "      <td>0.461353</td>\n",
       "      <td>0.418972</td>\n",
       "      <td>0.543407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>love_emolex</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sadness_emolex</th>\n",
       "      <td>0.512860</td>\n",
       "      <td>0.499632</td>\n",
       "      <td>0.413423</td>\n",
       "      <td>0.343349</td>\n",
       "      <td>0.495805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surprise_emolex</th>\n",
       "      <td>-0.668394</td>\n",
       "      <td>-0.671076</td>\n",
       "      <td>-0.529495</td>\n",
       "      <td>-0.636309</td>\n",
       "      <td>-0.647920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trust_emolex</th>\n",
       "      <td>0.286372</td>\n",
       "      <td>0.297457</td>\n",
       "      <td>0.350476</td>\n",
       "      <td>0.194851</td>\n",
       "      <td>0.321231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bias_sanctity  bias_fairness  bias_authority  \\\n",
       "anger_emolex              0.590114       0.550652        0.476331   \n",
       "anticipation_emolex      -0.061843       0.007693       -0.060699   \n",
       "disgust_emolex            0.077859      -0.020316       -0.057082   \n",
       "fear_emolex              -0.018212      -0.072020       -0.125618   \n",
       "joy_emolex                0.559410       0.540777        0.461353   \n",
       "love_emolex                    NaN            NaN             NaN   \n",
       "sadness_emolex            0.512860       0.499632        0.413423   \n",
       "surprise_emolex          -0.668394      -0.671076       -0.529495   \n",
       "trust_emolex              0.286372       0.297457        0.350476   \n",
       "\n",
       "                     bias_loyalty  bias_care  \n",
       "anger_emolex             0.407782   0.554558  \n",
       "anticipation_emolex     -0.203398  -0.018462  \n",
       "disgust_emolex          -0.072467  -0.017029  \n",
       "fear_emolex             -0.228606  -0.097623  \n",
       "joy_emolex               0.418972   0.543407  \n",
       "love_emolex                   NaN        NaN  \n",
       "sadness_emolex           0.343349   0.495805  \n",
       "surprise_emolex         -0.636309  -0.647920  \n",
       "trust_emolex             0.194851   0.321231  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Corrlation b/t MF bias and Emolex')\n",
    "corr.loc[emolex_cols,mf_bias]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "670c7cea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corrlation b/t MF bias and SpanEmo\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bias_sanctity</th>\n",
       "      <th>bias_fairness</th>\n",
       "      <th>bias_authority</th>\n",
       "      <th>bias_loyalty</th>\n",
       "      <th>bias_care</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>anger</th>\n",
       "      <td>0.429216</td>\n",
       "      <td>0.372502</td>\n",
       "      <td>0.393659</td>\n",
       "      <td>0.306543</td>\n",
       "      <td>0.389833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anticipation</th>\n",
       "      <td>0.565472</td>\n",
       "      <td>0.563723</td>\n",
       "      <td>0.511361</td>\n",
       "      <td>0.513794</td>\n",
       "      <td>0.566313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disgust</th>\n",
       "      <td>0.211182</td>\n",
       "      <td>0.174942</td>\n",
       "      <td>0.192563</td>\n",
       "      <td>0.127262</td>\n",
       "      <td>0.188710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fear</th>\n",
       "      <td>-0.757140</td>\n",
       "      <td>-0.736075</td>\n",
       "      <td>-0.688434</td>\n",
       "      <td>-0.663423</td>\n",
       "      <td>-0.743609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>joy</th>\n",
       "      <td>0.782519</td>\n",
       "      <td>0.764004</td>\n",
       "      <td>0.699598</td>\n",
       "      <td>0.693733</td>\n",
       "      <td>0.758731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>love</th>\n",
       "      <td>0.738770</td>\n",
       "      <td>0.708237</td>\n",
       "      <td>0.656388</td>\n",
       "      <td>0.672667</td>\n",
       "      <td>0.711879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>optimism</th>\n",
       "      <td>0.850778</td>\n",
       "      <td>0.839102</td>\n",
       "      <td>0.787424</td>\n",
       "      <td>0.776339</td>\n",
       "      <td>0.841192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pessimism</th>\n",
       "      <td>0.702543</td>\n",
       "      <td>0.597268</td>\n",
       "      <td>0.543473</td>\n",
       "      <td>0.559528</td>\n",
       "      <td>0.602133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sadness</th>\n",
       "      <td>0.299127</td>\n",
       "      <td>0.275260</td>\n",
       "      <td>0.281796</td>\n",
       "      <td>0.370214</td>\n",
       "      <td>0.278456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surprise</th>\n",
       "      <td>-0.240979</td>\n",
       "      <td>-0.297352</td>\n",
       "      <td>-0.194732</td>\n",
       "      <td>-0.154613</td>\n",
       "      <td>-0.252696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trust</th>\n",
       "      <td>0.551415</td>\n",
       "      <td>0.556491</td>\n",
       "      <td>0.578924</td>\n",
       "      <td>0.585433</td>\n",
       "      <td>0.586892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              bias_sanctity  bias_fairness  bias_authority  bias_loyalty  \\\n",
       "anger              0.429216       0.372502        0.393659      0.306543   \n",
       "anticipation       0.565472       0.563723        0.511361      0.513794   \n",
       "disgust            0.211182       0.174942        0.192563      0.127262   \n",
       "fear              -0.757140      -0.736075       -0.688434     -0.663423   \n",
       "joy                0.782519       0.764004        0.699598      0.693733   \n",
       "love               0.738770       0.708237        0.656388      0.672667   \n",
       "optimism           0.850778       0.839102        0.787424      0.776339   \n",
       "pessimism          0.702543       0.597268        0.543473      0.559528   \n",
       "sadness            0.299127       0.275260        0.281796      0.370214   \n",
       "surprise          -0.240979      -0.297352       -0.194732     -0.154613   \n",
       "trust              0.551415       0.556491        0.578924      0.585433   \n",
       "\n",
       "              bias_care  \n",
       "anger          0.389833  \n",
       "anticipation   0.566313  \n",
       "disgust        0.188710  \n",
       "fear          -0.743609  \n",
       "joy            0.758731  \n",
       "love           0.711879  \n",
       "optimism       0.841192  \n",
       "pessimism      0.602133  \n",
       "sadness        0.278456  \n",
       "surprise      -0.252696  \n",
       "trust          0.586892  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Corrlation b/t MF bias and SpanEmo')\n",
    "corr.loc[spanemo_cols,mf_bias]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d501825",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}