Bi-directional LSTM–CNN Combined method for Sentiment Analysis in Part of Speech Tagging (PoS)

Abstract

In past few years, the popularity of social media has increased drastically, sentiment analysis on the reviews, comments and opinions from social media has become more active in research area. A high grade, sentiment analysis portrays the opinion about the real time objects, topics, products and tweet reviews. The social trends or customer opinion is better understood with sentiment analysis. The state-of-art methods in analyzing the sentiments are based on textual features and with different neural network models. In this paper, we demonstrate and generalize a model combining bi-directional long short term memory (LSTM) and convolutional neural network (CNN), as bi-directional LSTM used to hold the temporal data for part-of-speech (PoS) tagging and CNN to extract the potential features. The experiment results validate our combined model performance with individual models. Our combined model indicates performance accurately and efficiently, achieving a reduced execution time and increased accuracy rate 98.6% in sentiment analysis is achieved by using combined bi-directional LSTM-CNN technique as when compared with traditional techniques.