Fuzzy Interpretation of Word Polarity Scores for Unsupervised Sentiment Analysis
Srishti Vashishtha, Seba Susan
Abstract
Sentiment or Opinion Mining aims to determine the polarity of people's opinions, feeling towards any product, service, event or any individual. One of the most popular technique applied in sentiment analysis of textual content is natural language processing. Sentiment can be evaluated using numerous methodologies like machine learning algorithms and statistical tools but the use of the fuzzy concept is not common. In this paper, we analyze the effect of fuzzification of word polarity sentiment scores. These word scores are obtained by deploying two lexicons: SentiWordNet and AFINN. Experiments are conducted on three benchmark datasets: polarity movie dataset by Pang-Lee, IMDB and hotel reviews dataset. The key highlights are: i) proposed an unsupervised fuzzy logic-based approach for sentiment analysis of textual reviews, ii) the proposed model formulated fuzzy cardinality as the measure for the evaluation of word polarity scores, iii) our model has two versions based on the sentiment lexicon deployed in the model, iv) comparison of our fuzzy cardinality approach with other non-fuzzy state-of-the-art methods reveals the superiority of our fuzzy approach.