/LinkedIn-Analysis

About LinkedIn Sentiment Analysis project analyzes user posts, provides insights, and compares performance, aiding professionals in understanding engagement and trends.

Primary LanguagePython

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.