Feature_Extraction1.ipynb

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        "\n",
        "# 📊 Proyecto de Análisis de Sentimientos de Tweets"
      ],
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        "## 🎯 Problema de Negocio\n",
        "\n",
        "El objetivo de este proyecto es analizar los sentimientos de los tweets, identificando si los mensajes son positivos o negativos. Utilizando un conjunto de datos de 1,600,000 tweets preprocesados, se pretende generar diversas características que se utilizarán para entrenar un modelo de clasificación."
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      "source": [
        "## ❓ Preguntas Clave\n",
        "\n",
        "- **¿Qué variables pueden ser clave para la detección del sentimiento?**\n",
        "- **¿Qué tipo de modelos pueden servir para detectar el sentimiento en los tweets?**\n",
        "- **¿Qué métricas se pueden utilizar para evaluar el desempeño del modelo?**"
      ],
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      "source": [
        "import pandas as pd\n",
        "import re\n",
        "import string\n",
        "import emoji\n",
        "from nltk.corpus import stopwords\n",
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "import nltk\n",
        "import math\n",
        "from textblob import TextBlob\n",
        "nltk.download('stopwords')\n",
        "\n",
        "# Cargar el dataset de tweets\n",
        "df = pd.read_csv(\"training_1600000_processed_noemoticon.csv\", encoding='latin-1')\n",
        "df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']\n",
        "df"
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              "         target         ids                          date      flag  \\\n",
              "0             0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   \n",
              "1             0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY   \n",
              "2             0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
              "3             0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
              "4             0  1467811372  Mon Apr 06 22:20:00 PDT 2009  NO_QUERY   \n",
              "...         ...         ...                           ...       ...   \n",
              "1599994       4  2193601966  Tue Jun 16 08:40:49 PDT 2009  NO_QUERY   \n",
              "1599995       4  2193601969  Tue Jun 16 08:40:49 PDT 2009  NO_QUERY   \n",
              "1599996       4  2193601991  Tue Jun 16 08:40:49 PDT 2009  NO_QUERY   \n",
              "1599997       4  2193602064  Tue Jun 16 08:40:49 PDT 2009  NO_QUERY   \n",
              "1599998       4  2193602129  Tue Jun 16 08:40:50 PDT 2009  NO_QUERY   \n",
              "\n",
              "                    user                                               text  \n",
              "0          scotthamilton  is upset that he can't update his Facebook by ...  \n",
              "1               mattycus  @Kenichan I dived many times for the ball. Man...  \n",
              "2                ElleCTF    my whole body feels itchy and like its on fire   \n",
              "3                 Karoli  @nationwideclass no, it's not behaving at all....  \n",
              "4               joy_wolf                      @Kwesidei not the whole crew   \n",
              "...                  ...                                                ...  \n",
              "1599994  AmandaMarie1028  Just woke up. Having no school is the best fee...  \n",
              "1599995      TheWDBoards  TheWDB.com - Very cool to hear old Walt interv...  \n",
              "1599996           bpbabe  Are you ready for your MoJo Makeover? Ask me f...  \n",
              "1599997     tinydiamondz  Happy 38th Birthday to my boo of alll time!!! ...  \n",
              "1599998   RyanTrevMorris  happy #charitytuesday @theNSPCC @SparksCharity...  \n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El dataset contiene texto que se puede considerar como \"sucio\", es por ello que partamos de crear una función que pueda limpiar este texto eliminando URLs, menciones, emojis y caracteres especiales.\n",
        "Recuerda que la limpieza de texto es crucial para que el análisis sea más preciso."
      ],
      "metadata": {
        "id": "jMIXw8hRj6Cv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(text):\n",
        "    # Aquí el alumno debe eliminar URLs del texto usando expresiones regulares.\n",
        "    # También se debe eliminar las menciones de otros usuarios en el texto (@usuario).\n",
        "    # Es importante eliminar los emojis y convertir todo el texto a minúsculas.\n",
        "    pass\n",
        "df['cleaned_text'] = df['text'].apply(clean_text)"
      ],
      "metadata": {
        "id": "QoJSN_K6i-1m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Longitud del tweet: se debe calcular la longitud del texto del tweet en su versión limpia (número de caracteres).\n",
        "Esta característica es útil porque podría revelar patrones sobre la longitud y el contenido de los tweets."
      ],
      "metadata": {
        "id": "JbntRAB8kVeM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tweet_length(text):\n",
        "    # Aquí el alumno debe devolver la longitud del texto, es decir, cuántos caracteres tiene el tweet.\n",
        "    pass\n",
        "df['cleaned_text'] = df['text'].apply(clean_text)"
      ],
      "metadata": {
        "id": "xTG-q-85kTZu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Contar emojis: en algunos casos, los emojis pueden representar emociones, por lo tanto, contamos cuántos emojis hay en el tweet.\n",
        "La función debe recorrer cada carácter del texto y contar cuántos son emojis."
      ],
      "metadata": {
        "id": "QzFVyv2ClEEX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def count_emojis(text):\n",
        "    # El alumno debe contar cuántos emojis aparecen en el tweet original.\n",
        "    pass\n",
        "df['emoji_count'] = df['text'].apply(count_emojis)"
      ],
      "metadata": {
        "id": "cEZufjvBlBjX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Contar signos de exclamación e interrogación: los tweets con muchos signos de exclamación o interrogación podrían indicar emoción o sorpresa."
      ],
      "metadata": {
        "id": "YKg9zstxkxZR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def exclamation_count(text):\n",
        "    # Contar cuántos signos de exclamación (!) hay en el tweet.\n",
        "    pass\n",
        "\n",
        "def question_count(text):\n",
        "    # Contar cuántos signos de interrogación (?) hay en el tweet.\n",
        "    pass\n",
        "\n",
        "df['exclamation_count'] = df['text'].apply(exclamation_count)\n",
        "df['question_count'] = df['text'].apply(question_count)"
      ],
      "metadata": {
        "id": "AyrU3QNW7dXJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Es tu turno! ¿Qué otras variables se te ocurren para poder construir un modelo que pueda predecir el sentimiento del tweet?"
      ],
      "metadata": {
        "id": "q20ejZbely1q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Existen librerías que ya detectan los sentimientos de texto, crees que debas de usarlas?**"
      ],
      "metadata": {
        "id": "mhpp87IVm2ut"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('datatotal.csv')"
      ],
      "metadata": {
        "id": "IsKQuapuCUmy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}