Sentiment Hub

Sentiment Hub is a sentiment analysis tool designed to analyze emotions expressed in text comments on YouTube and Instagram. With the ability to detect six different emotions, it provides valuable insights into audience reactions and sentiment trends across these platforms.

This project aims to provide an easy-to-use interface for sentiment analysis, allowing users to gain deeper understanding and actionable insights from social media interactions.

Key features:

• Analyze sentiment across YouTube and Instagram comments.
• Detect six different emotions: happiness, sadness, anger, fear, surprise, and disgust.
• Easy-to-use Chrome Extension that automatically detects that you are on a social media site and analyses Post and comments
• User-friendly interface for input and visualization of results.
• Data visualization of analysing emotions in Pie-charts , wordClouds , etc..
• Target based analysis on specific inputs

Ready to get started? Follow the installation steps below to set up Sentiment Hub and start analysing sentiment today!

Technologies used

• Python
• Matplotlib
• Hugging Face
• Javascript
• Node JS
• Docker
• Streamlit

Description

The Sentiment Hub project is an emotion detection tool mainly focusing on Social Media platforms ( Youtube and Instagram ) using Sentiment Analysis. Table of Contents • Installation • Usage • Contributing • License •

Installation

i) Installation process for Chrome Extension

• clone the repository containing the code files for the Chrome extension
• enable the developers tool in Chrome manage extension
• load the unpack of the cloned repository and you are good to go ...

ii) Installation process for streamlit website

• create a python virtual environment

• clone the repository in the created virtual environment

• pip install requirements.txt

  $ pip install -r requirements.txt

  $ npm i

• open terminal and run the command

   $ streamlit run ./model/sentiment_analysis/app.py

• and you are good to analyze

Visualizations

• Pie Charts: Visual representation of sentiment distribution
• Word Clouds : Graphical representation of frequently occuring words in positive , negative and neural comments
• Tabular : direct table format for the result of 6 emotion sentiment analysis

Download Result

• Click the " Download Results " button to auto download the analysis result in CSV format ( the above is for the Sentiment analysis using website )

Usage

1. Using Chrome extension

• After enabling the Chrome Extension , surf on Youtube videos to analyse automatically the active tab video comments
• just on one click analyze the sentiments of the comments

2. Using the Streamlit website

• run the application :

  $ streamlit run ./model/sentiment_analysis/app.py

• explore sentiment analysis , visualization and download results using the provided options

Contact

• Team Leader : Name : Tanmay Shingavi email : tanmayshingavi1@gmail.com contact :7448267050

• Member 1 : Name : Yash Bhoomkar email : bhoomkar04@gmail.com contact : 9405390030

• Member 2 : Name : Swaroop Pokharkar email : swarooppokharkar8@gmail.com

• Member 3 : Name : Amey Joshi email : ameyj3068@gmail.com conatct : 8208807387

Screenshots