An attention-based deep learning method for text sentiment analysis

Abstract

Text sentiment analysis is target-oriented, aiming to identify the opinion or attitude from a piece of natural language text toward topics or entities, whether it is negative, positive or neutral using natural language processing and computational methods. With the growth of the internet, numerous business websites have been deployed to support shopping products, booking services online as well as to allow online reviewing and commenting the services in forms of either business forums or social networks. Use of text sentiment analysis for automatically mining opinion from the feedbacks on such emerging internet platforms is not only useful for customers seeking for advice, but also necessary for business to study customers’ attitudes toward brands, products, services, or events, and has become an increasingly dominant trend in business strategic management. Current state-of-the-art approaches for text sentiment analysis include lexicon based and machine learning based methods. In this research, we proposed a method that utilizes deep learning with attention word embedding. We showed that our method outperformed popular lexicon and embedding based methods.