A Systematic Review on Hidden Markov Models for Sentiment Analysis

Abstract

This paper gives a review of the literature on the application of Hidden Markov Models in the field of sentiment analysis. This is done in relation to a research project on semantic representation and the use of probabilistic graphical models for the determination of sentiment in textual data. Relevant articles have been analyzed that correspond mainly to the certain variations of the implementation of HMM and a variety of use cases for the purpose of sentiment classification. Finally, this review presents the grounds for future works that seek to develop techniques for semantic text representations implemented with probabilistic graphical models (Hidden Markov Models) or that through a combination scheme allow for superior classification performance.