Unsupervised Sentiment Analysis to Extract Topics and Their Semantic Orientations from Natural Language Text

Abstract

Sentiment analysis is a technique that analyzes and extracts the evaluation information for products and services from text data. In the present paper, we propose variations of unsupervised sentiment analysis models that can estimate semantic orientations of comments (whether they are either positive or negative) together with their topics by processing text data without any ratings. The proposed models extend a standard topic model by introducing dictionaries of emotional words so that it can analyze in what point of view people are evaluating subjects positively or negatively.