XSentix

Overview

XSentimentAnalyzer is a powerful sentiment analysis and content recommendation system designed to analyze and interpret sentiments expressed in tweets on the "X" platform (formerly known as Twitter).

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Key Features

• Sentiment Analysis: XSentimentAnalyzer utilizes state-of-the-art NLP models to perform in-depth sentiment analysis on tweets, categorizing them as positive, negative, or neutral.

• Emotion Classification: Beyond sentiment, our system classifies tweets into various emotions, including joy, sadness, anger, and more, offering a richer understanding of user sentiments.

• Sensitive Content Detection: XSentimentAnalyzer actively scans tweets for sensitive information such as mentions of critical events (e.g., death) and, when detected, initiates notifications to appropriate individuals or groups.

• Personalized Content Recommendations: Based on a user's current sentiment or emotional state, XSentimentAnalyzer recommends tailored content, including news articles, videos, and user profiles to follow, enhancing the user experience.

• Real-time Updates: The system continuously updates sentiment scores, emotion classifications, and content recommendations, ensuring users receive the most relevant information as it happens.

• Interactive Dashboard: XSentimentAnalyzer provides a comprehensive, user-friendly dashboard with interactive visualizations, including graphs and charts, to present tweet data in an easily digestible format.

Usage

• Access the web-based interface or use API endpoints to input a user's "X" platform username or a specific hashtag.
• XSentimentAnalyzer will perform sentiment analysis on recent tweets associated with the user or hashtag.
• The system will provide sentiment scores, emotion classifications, and sensitive content detection.
• If sensitive information is detected, notifications will be sent to designated contacts or well-wishers.
• Explore the interactive dashboard to gain insights through visually appealing graphs and charts.

Getting Started

These instructions will help you set up a virtual environment and install the required dependencies for this project.

Prerequisites

• Python (>=3.6) installed on your system.
• Git (optional, if you plan to clone the repository).

Setting up a Virtual Environment

It's a good practice to create a virtual environment to manage project dependencies. You can use venv or virtualenv depending on your preference.

Using venv (Python 3.3+)

python -m venv myenv  # Replace 'myenv' with your preferred environment name
source myenv/bin/activate  # Use 'activate' on Windows

pip install virtualenv
virtualenv myenv  # Replace 'myenv' with your preferred environment name
source myenv/bin/activate  # Use 'activate' on Windows

Installing Dependencies

Once you have activated your virtual environment, you can install the project dependencies using the requirements.txt file.

pip install -r requirements.txt

This will install all the required packages for the project.

Contributors

• Ashish Meena
• Deepak Goyal
• Divyanshi Rajpurohit
• Diya Vijay

License

This project is open-source and licensed under the MIT License.