Sentiment Analysis Project

Overview

This project is a Sentiment Analysis tool that classifies the sentiment of text data as positive, negative, or neutral. Sentiment analysis is a natural language processing (NLP) technique used to determine the emotional tone behind words. This can help in understanding the sentiment of the text, such as product reviews, social media posts, or any other text-based data.

Features

• Text Preprocessing: The script includes functions to clean and preprocess the text data, which includes removing stop words, punctuation, and applying stemming or lemmatization techniques.
• Modeling: It implements a machine learning model to predict the sentiment of a given text. The model is trained on labeled data and can classify new text inputs.
• Evaluation: The script evaluates the performance of the sentiment analysis model using common metrics such as accuracy, precision, recall, and F1-score.

Prerequisites

To run this project, ensure you have the following installed:

• Python 3.x
• The required Python libraries.

Usage

1. Clone the Repository:

   git clone https://github.com/your-username/sentiment-analysis.git
   cd sentiment-analysis

2. Run the Script:

   To analyze sentiment on a sample text, run:

   python sentiment_analysis.py

3. Custom Input:

   You can modify the script to analyze your custom text or data by updating the text variable in the script.

Project ScreenShots

Files in the Repository

• sentiment_analysis.py: The main script containing the sentiment analysis implementation.
• README.md: This file, providing an overview of the project.

Contributing

Contributions are welcome! If you'd like to contribute, please fork the repository and make changes as you'd like. Pull requests are encouraged.

Contact

If you have any questions, feel free to contact me at [email protected].