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.