Skip to content

Latest commit

 

History

History
100 lines (55 loc) · 2.59 KB

readme.md

File metadata and controls

100 lines (55 loc) · 2.59 KB
Raw

Sentiment Analysis Project 📊💬

Welcome to my sentiment analysis project! This project analyzes the sentiment of text using machine learning techniques and natural language processing. We'll walk you through the process step by step.

Overview 🌟

Sentiment analysis is the process of determining the sentiment expressed in a piece of text. In this project, we utilize a variety of tools and techniques to analyze sentiments, from data collection to model evaluation.

Steps Involved 🛠️

1. Data Collection

We collect text data from a file for analysis.

2. Data Pre-processing and Labeling

Text data undergoes pre-processing, including lowercasing, punctuation removal, and labeling with sentiment.

3. Feature Extraction

We use TF-IDF vectorization to extract features from the text data.

4. Model Selection and Training

A Support Vector Machine (SVM) model is selected and trained using the labeled data.

5. Model Evaluation and Prediction

The trained model is evaluated using test data, and predictions are made.

Sentiment Analysis Function 📝

We provide a function to analyze the sentiment of any text input. It utilizes the VADER sentiment analysis tool.

Overall Sentiment Analysis 📈

We determine the overall sentiment of the text and display it.

Emotion Distribution Plot 📊

A bar plot is generated to visualize the distribution of emotions in the analyzed text.

Repository Structure 📁


sentiment-analysis/
│
├── emotions.txt
├── read.txt
├── sentiment_analysis.ipynb
└── README.md

Usage Instructions 📝

  1. Clone the repository:

    git clone https://github.com/j-a-y-e-s-h/sentiment-analysis.git

  2. Install dependencies:

   pip install -r requirements.txt
  1. Run the Python script to perform sentiment analysis.
   python sentiment_analysis.py
  1. Change data. If you want to change data or analysis in new data. change the data in read.txt file

Contribution 🤝

Contributions are welcome! Feel free to fork this repository and submit pull requests.

Credits 🙏

This project utilizes the NLTK library, scikit-learn, and matplotlib for sentiment analysis and visualization.

Feel free to explore the project and provide feedback! If you find it useful, don't forget to give it a ⭐️!

All Rights Reserved 📝🔒

All rights reserved to Jayesh.

For more information, visit Jayesh's GitHub Profile 🌟.