A Novel Approach for Sentiment Analysis Using Deep Recurrent Networks and Sequence Modeling

Abstract

Background: Due to the increasing growth of social websites, a lot of user-generated data is available these days in the form of customer reviews, opinions, and comments. Objective: Sentiment analysis includes analyzing the user reviews and finding the overall opinions from the reviews in terms of positive, negative and neutral categories. Sentiment analysis techniques can be used to assign a piece of text a single value that represents opinion expressed in that text. Sentiment analysis using lexicon approaches is already studied. Methods: A new approach to sentiment analysis using deep neural networks techniques is proposed. Deep neural networks using Sequence to sequence model is studied in this paper. The main objective of this paper is to identify the sequence of relationships among the words in the reviews. Customer reviews are taken from Amazon and sentiment analysis is done using the word embedding method. Results: The results obtained by the proposed method are compared with the baseline algorithms such as Naïve, and logistic regression. Conclusion: Confusion Matrix along with receiver operating characteristics and area under the curve is analyzed. The accuracy of the proposed methodology is compared with other algorithms.