Comparison of RNN Architectures and Non-RNN Architectures in Sentiment Analysis

Abstract

This study compares the sentiment analysis performance of multiple Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks. THE METHODS EVALUATED ARE simple Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Recurrent Neural Network, and 1D ConvNets. A dataset comprising text reviews with positive or negative sentiment labels was evaluated. All evaluated models demonstrated an extremely high accuracy, ranging from 99.81% to 99.99%. Apart from that, the loss generated by these models is also low, ranging from 0.0043 to 0.0021. However, there are minor performance differences between the evaluated architectures. The Long Short-Term Memory and Gated Recurrent Unit models mainly perform marginally better than the Simple Recurrent Neural Network, albeit with slightly lower accuracy and loss. In the meantime, the Bidirectional Recurrent Neural Network model demonstrates competitive performance, as it can effectively manage text context from both directions. Additionally, One-Dimensional Convolutional Neural Networks provide satisfactory results, indicating that convolution-based approaches are also effective in sentiment analysis. The findings of this study provide practitioners with essential insights for selecting an appropriate architecture for sentiment analysis tasks. While all models yield excellent performance, the choice of architecture can impact computational efficiency and training time. Therefore, a comprehensive comprehension of the respective characteristics of Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks is essential for making more informed decisions when constructing sentiment analysis models.