This repository contains a Notebook for classifying sentiments (positive or negative) in tweets using a variation of the Sentiment140 dataset. The project includes data preprocessing, tokenization, padding, and model training using a Bidirectional LSTM with pre-trained GloVe embeddings.
This project aims to classify the sentiment of tweets as either positive or negative. It involves preprocessing the Sentiment140 dataset, tokenizing the text, padding sequences, and training a Bidirectional LSTM model using pre-trained GloVe embeddings.
The dataset used is a variation of the Sentiment140 dataset, which contains 1.6 million tweets labeled with their sentiment (0 for negative and 4 for positive).
The model's performance is evaluated using training and validation accuracy and loss, with visualizations provided for both metrics over 20 epochs.