Sentiment analysis using novel and interpretable architectures of Hidden Markov Models

Abstract

Sentiment analysis aims to formulate automated methods to recognize sentiments, opinions and emotions in text. Many methods and approaches have been utilized but most of them do not disclose the way that decisions are made and operate as black boxes. Hidden Markov Models (HMMs) constitute a quite suitable and potent approach for sentiment analysis, since they can utilize the sequential nature of the text, a piece of information that machine learning methods cannot properly utilize. However, little attention has been paid to formulating and applying sophisticated HMM-based methods and advanced training approaches for accomplishing sentiment analysis tasks. In this article, we introduce novel, interpretable HMM-based methods for recognizing sentiments in text and we examine their performance under various architectures, training methods, orders and ensembles. The introduced models possess interpretability, they can indicate the sentimental parts of a sentence and illustrate the way that the overall sentiment evolves from the start to the end of it. A concrete experimental study is conducted and the results show that the introduced HMMs methods and the training approaches are quite competitive with machine learning methods and that they outperform traditional HMMs. Furthermore, the designed HMMs methods possess great interpretability and can be an efficient approach for sentiment analysis.