https://github.com/nikhilgugwad/sentiment-analysis

Sentiment analysis for the Kannada language to classify Kannada sentences into different emotions.
https://github.com/nikhilgugwad/sentiment-analysis

numpy pandas scikit-learn

Last synced: about 1 month ago
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Sentiment analysis for the Kannada language to classify Kannada sentences into different emotions.

• Host: GitHub
• URL: https://github.com/nikhilgugwad/sentiment-analysis
• Owner: nikhilgugwad
• Created: 2023-07-21T14:25:09.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-01T11:01:15.000Z (about 2 months ago)
• Last Synced: 2024-12-01T12:18:47.232Z (about 2 months ago)
• Topics: numpy, pandas, scikit-learn
• Language: Python
• Homepage:
• Size: 10.2 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Sentiment Analysis for Indian Regional Language

This project focuses on sentiment analysis for the Kannada language. The main goal is to classify input Kannada sentences into different emotions such as sad, happy, fear, and angry. Sentiment analysis is a subfield of natural language processing that involves determining the sentiment or emotion expressed in a given piece of text.

**Note:** This project is focused on sentiment analysis for Kannada language text. It does not include a graphical user interface (GUI) for interaction. The output of the sentiment analysis is displayed in the IDE terminal when the provided script is run.

If you're looking for a GUI-based application, this project might not meet that requirement. However, if you're interested in programmatically analyzing Kannada text sentiment, you'll find this project's script useful.

## Features

- Input: Kannada sentences

- Output: Predicted emotions (sad, happy, fear, angry)

## How It Works

1. **Data Collection**: Gather a dataset of Kannada sentences labeled with emotions (e.g., sad, happy, fear, angry).

2. **Preprocessing**: Clean and preprocess the text data, including tasks like tokenization, removing stop words, and stemming.

3. **Model Training**: Train a sentiment analysis model using machine learning or deep learning techniques. Popular approaches include using recurrent neural networks (RNNs) or transformer models.

4. **Inference**: Deploy the trained model to make predictions on new Kannada sentences.

5. **Visualization**: Visualize the predicted emotions using graphs or charts to provide insights into the sentiment distribution.