LSTM-based Sentiment Analysis

Overview

This project focuses on classifying sentiment from social media text into positive, neutral, and negative categories using deep learning. The main goal is to build a robust sentiment analysis model that can handle noisy and sequential text data.

What I Built

• An LSTM-based sentiment classification model for multi-class sentiment prediction
• Text preprocessing pipeline including cleaning, tokenization, and sequence padding
• Model training and evaluation using accuracy and classification metrics
• Analysis of model performance to ensure stability and generalization on real-world data

Tools & Technologies

• Python – data preprocessing and experimentation
• Keras – LSTM model development and training
• Natural Language Processing (NLP) – text cleaning, tokenization, and sequencing