Fuzzy rule based unsupervised sentiment analysis from social media posts. (30th December 2019) - Vashishtha, Srishti; Susan, Seba
Highlights: A fuzzy rule-based approach is proposed for sentiment analysis of tweets. Formulation of nine fuzzy rules to compute sentiment of each tweet. The proposed unsupervised approach is suitable for any sentiment lexicon. The approach can be adapted to both bipolar and tripolar sentiment analysis tasks. Abstract: In this paper, we compute the sentiment of social media posts using a novel set of fuzzy rules involving multiple lexicons and datasets. The proposed fuzzy system integrates Natural Language Processing techniques and Word Sense Disambiguation using a novel unsupervised nine fuzzy rule based system to classify the post into: positive, negative or neutral sentiment class. We perform a comparative analysis of our method on nine public twitter datasets, three sentiment lexicons, four state-of-the-art approaches for unsupervised Sentiment Analysis and one state-of-the-art method for supervised machine learning. Traditionally, Sentiment Analysis of twitter data is performed using a single lexicon. Our results can give an insight to researchers to choose which lexicon is best for social media. The fusion of fuzzy logic with lexicons for sentiment classification provides a new paradigm in Sentiment Analysis. Our method can be adapted to any lexicon and any dataset (two-class or three-class sentiment). The experiments on benchmark datasets yield higher performance for our approach as compared to the state-of-the-art.