Regular paper Comparative Analysis of Deep Learning Models for Sentiment Analysis on IMDB Reviews

Abstract

Within the domain of natural language processing, sentiment analysis assumes a fundamental position, facilitating the comprehension of societal perspectives and opinions, thereby playing a pivotal role in understanding public sentiment. In this study, an examination and comparison of deep learning architectures was conducted on IMDB movie reviews. We evaluated the performance of Basic Recurrent Neural Networks, Long Short-Term Memory (LSTM), Gated Recurrent Neural Unit (GRU), Bidirectional LSTM, Bidirectional GRU and 1D Convolutional Neural Networks (Conv1D) based on their training, validation, and testing accuracies. Our results indicate that while LSTM achieved the highest accuracy of 99.91% on the training data, GRU demonstrated superior performance (88.28%) on the validation dataset. Interestingly, Bidirectional GRU emerged as the top-performing model (87.54%) on the testing data, showcasing its robustness in generalizing to unseen instances. These findings highlight the importance of evaluating model performance across multiple datasets to assess their real-world effectiveness. Furthermore, our comparative analysis provides valuable understanding of the advantages and limitations of each model, offering practical guidance for selecting the optimal framework for sentiment analysis endeavors. Overall, this research contributes to the progress of such methodologies and deep learning approaches in natural language processing.