Sentiment and Context-Aware Recurrent Convolutional Neural Network for Sentiment Analysis

Sentiment analysis is a Natural Language Processing (NLP) task that involves using machine learning techniques, particularly deep learning, to determine the sentiment or emotion expressed in a piece of text. The goal of sentiment analysis is to understand whether the text expresses a positive, negative, or neutral sentiment towards a particular subject, product, service, or topic. A deep learning technique called Sentiment and Context-Aware Hybrid Deep Neural Network (SCA-HDNN) was proposed for sentiment analysis. In SCA-HDNN, Convolutional Neural Network (CNN) was used for sentiment classification. However, CNNs require fixed-sized inputs, which can be a limitation when dealing with sequences of different lengths. So in this paper, Recurrent CNN (RCNN) is introduced for sentiment classification. The incorporation of recurrent connections in each convolutional layer of RCNN enables handling variable-length sequences. RCNNs can be more computationally efficient than CNN. The combination of CNNs and RNNs allows for parallelization and shared computations across time steps, which can speed up training and inference for sentiment analysis. This method is named as SCA-RCNN. The results from the experiment demonstrate that the suggested SCA-RCNN method achieves high levels of accuracy, precision, and recall in sentiment analysis.