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LinkedIn Sentiment Analysis

Introduction

Welcome to the LinkedIn Sentiment Analysis project! This repository aims to perform sentiment analysis on LinkedIn data, extracting insights from user posts and interactions.

Features

  • Profile metrics dashboard
    • Shows summary stats for your profile: likes, appreciations, impressions etc.
    • Top posts ranked by engagement
    • Historical trends over time
  • Post analysis
    • Sentiment analysis of comments using AI
    • Visualizations of reactions and engagement
  • Competitor benchmarking
    • Extract comments, profiles from competitor pages
    • Analysis to compare performance vs competitors

Prerequisites

You need to install:

  • Python 3.7+
  • Streamlit
  • Pandas, Numpy etc for data analysis
  • Selenium for web scraping LinkedIn pages

Register for these APIs:

  • LinkedIn data API to extract profile/post metrics
  • AI text analysis API for sentiment analysis

Installation

To use this project locally, follow these steps:

  1. Clone the repository:

    git clone https://github.com/Venkateeshh/LinkedIn-Sentiment-Analysis.git

  2. Navigate to the project directory:

    cd LinkedIn-Sentiment-Analysis

  3. Install dependencies:

    # Add installation commands if any

Usage

The sidebar menu allows choosing different analysis options:

My Info: Enter your LinkedIn URL. Fetches profile metrics and top posts ranked by engagement.

Post Analysis: Enter any LinkedIn post URL. Fetches comments and analyzes sentiment.

Competitor Analysis: Enter competitor profile username and login creds. Extracts comments, profiles and analyzes to benchmark vs your profile.

Run Locally

streamlit run app.py

It will open a browser window at localhost:8501 with the dashboard.

Features

Highlight the key features of your project.

  • Sentiment analysis on LinkedIn posts.
  • Automated post scheduling
  • Job search integration
  • Lead generation tracking
  • Multiple profile comparison

Contributing

If you'd like to contribute to this project, follow these steps:

  1. Fork the repository.
  2. Create a new branch: git checkout -b feature_branch.
  3. Make your changes and commit them: git commit -m 'Add some feature'.
  4. Push to the branch: git push origin feature_branch.
  5. Open a pull request.