Comparative Analysis of Logistic Regression, LSTM, and Bi-LSTM Models for Sentiment Analysis on IMDB Movie Reviews

Abstract

This study examines the efficacy of three distinct machine learning models for sentiment analysis: Bidirectional Long Short-Term Memory (Bi-LSTM), Long Short-Term Memory (LSTM), and Logistic Regression. One of the most important tasks in natural language processing is sentiment analysis, which entails categorizing reviews as positive or negative. Fifty thousand sentiment-labeled movie reviews make up the dataset. To determine the most precise and effective technique for sentiment categorization, we put these models into practice and evaluated their respective performances. The highest accuracy of 89.42% is achieved by Logistic Regression, with precision of 88.35%, recall of 91.01%, and F1-score of 89.66%. The LSTM model, well-known for capturing temporal dependencies in text, obtained an accuracy of 86.23%, precision of 89.60%, recall of 82.22%, and an F1-score of 85.75%. The Bi-LSTM model, which processes input sequences in both forward and backward directions to better capture context, demonstrated accuracy of 87.65%, precision of 88.67%, recall of 86.54%, and an F1-score of 87.60%. The results show that although Logistic Regression performs better in accuracy, the Bi-LSTM model offers a substantial trade-off between precision and recall, making it a viable option for sentiment analysis applications. Despite its minor accuracy lag, the LSTM model provides important insights into the sequential relationships of the text data.