Review of artificial neural network and recurrent neural network approaches for sentiment analysis in movie reviews

Sentiment analysis plays a crucial role in understanding and extracting subjective information from large amounts of text data, such as movie reviews. With the increasing popularity of deep learning models, artificial neural networks (ANNs) and recurrent neural networks (RNNs) have emerged as powerful tools for sentiment analysis tasks. This paper provides a comprehensive review of the application of ANNs and RNNs in sentiment analysis specifically focused on movie reviews. The review begins by discussing the fundamental concepts and principles of ANNs and RNNs, highlighting their respective architectures and learning mechanisms. It then delves into the various techniques and strategies employed in sentiment analysis using ANNs and RNNs. These techniques include feature extraction, text representation, and model training, which are critical components in achieving accurate sentiment classification. the review examines the performance of ANNs and RNNs in sentiment analysis tasks, considering factors such as dataset size, model complexity, and training duration. It also explores the impact of different neural network architectures, such as feedforward neural networks, convolutional neural networks, and long short-term memory networks, on sentiment analysis performance.