Sentiment Analysis Using Residual Learning with Simplified CNN Extractor

Abstract

Sentiment analysis has an important role in social media monitoring as it extracts public opinions, emotions, and feelings about certain products or services. There are several publications in building a system to identify opinions from text using rule-based approach, lexicon-based approach, or machine learning. In this paper, we propose and compare several deep learning models to solve sentiment analysis problem of the Internet Movie Database (IMDb) review sentiment dataset. The feature extractor consists of a convolutional layer, followed by a max pooling layer and a batch normalization layer. To solve the vanishing gradient problem, we use a residual connection to concatenate the input values with the extracted features before feeding the output into a recurrent layer. Our best model has an accuracy of 90.02%.