Sentiment Analysis Using Fuzzy-Deep Learning

Abstract

Sentiment Analysis also termed as opinion mining is a classification process which is used to determine the polarity associated with user reviews in the form of text or images or speech. Due to the rapid growth in usage of social media such as forums, social networks, micro blogs etc., the need for sentiment analysis has simultaneously increased. Sentiment analysis can be helpful in improving marketing strategy for a product or customer services, providing information about in general public sentiment for a political party or candidate etc. Over the years, various techniques have been developed to provide user with better sentiment classification. These techniques have evolved from lexicon based to machine learning and now to deep learning. But there is an inherent uncertainty in natural language which could not be handled even by the most advanced deep learning techniques. Deep learning networks perform automatic feature extraction from given data. But, fuzzy logic helps us to deal with this uncertainty by providing us with decision making capabilities in the presence of ambiguity. Our aim is to improve sentiment analysis prediction for textual data by incorporating fuzziness with deep learning. So, in this paper we have combined the learning capabilities of deep learning and uncertainty handling abilities of fuzzy logic to provide more appropriate sentiment prediction to the user. We have used LSTM, a type of Recurrent Neural Network (RNN) for sentiment prediction. These networks have helped us to improve prediction accuracy as they are capable of dealing with long-term dependencies in the data.