Emotion Detection Using Machine Learning

Overview

This project focuses on emotion detection by classifying user comments into different emotional categories using machine learning techniques. The model is trained on a Kaggle dataset containing labeled text data and achieves high accuracy in predicting emotions.

Features

• Text preprocessing (stop-word removal, lowercasing, special character removal).
• Feature extraction using TF-IDF and Bag of Words.
• Classification using Logistic Regression and Random Forest.
• Achieves 90% accuracy on the test dataset.

Dataset

The dataset consists of two columns:

• Text: User comments.
• Label: Numeric values (0–5) representing emotions such as Sad, Happy, Angry, etc.

Preprocessing Steps

• Removing stop words.
• Converting text to lowercase.
• Removing special characters and punctuation.

Model Training

The following machine learning models were implemented:

• Logistic Regression: A statistical model for classification.
• Random Forest: An ensemble learning technique that improves accuracy.

Performance

• The model achieved 90% accuracy on the test dataset.

Future Enhancements

• Implementing deep learning models like LSTMs.
• Using a larger, real-world dataset for better generalization.

Model Architecture

Installation & Usage

Requirements

• Python 3.x
• NumPy
• Pandas
• Scikit-learn
• Matplotlib

Steps to Run

1. Clone the repository:

   git clone https://github.com/your-username/emotion-detection.git
   cd emotion-detection