MoodMetric

Overview

A Python-based application that leverages a sentiment analysis tool to analyze the sentiment of social media text data. It provides insights into whether text content is positive, negative, neutral, or extremely positive/negative. The project includes three main components: a sentiment analysis module, a social media text analyzer, and a user-friendly application to run the analysis.

Table of Contents

• Features
• Usage
• Project Structure
• Contributing

Features

Sentiment Analysis

The project employs the sentiment analysis tool to categorize text data into the following sentiment labels:

• Extremely Positive
• Very Positive
• Positive
• Extremely Negative
• Very Negative
• Negative
• Neutral

Social Media Text Analysis

The SocialMediaAnalyzer class can analyze a list of social media texts and provide sentiment results for each text, making it suitable for batch processing.

User-Friendly Application

The SocialMediaApp class serves as an easy-to-use interface for running sentiment analysis on social media texts. It allows users to load data from a file, view sentiment results, visualize the sentiment distribution, and save results to an output file.

Usage

To use the Social Media Sentiment Analyzer, follow these steps:

1. Install the required Python dependencies (NLTK and Matplotlib) using pip:

pip install nltk matplotlib