MuSES: Multilingual Sentiment Elicitation System for Social Media Data

Abstract

A multilingual sentiment identification system (MuSES) implements three different sentiment identification algorithms. The first algorithm augments previous compositional semantic rules by adding rules specific to social media. The second algorithm defines a scoring function that measures the degree of a sentiment, instead of simply classifying a sentiment into binary polarities. All such scores are calculated based on a large volume of customer reviews. Due to the special characteristics of social media texts, a third algorithm takes emoticons, negation word position, and domain-specific words into account. In addition, a proposed label-free process transfers multilingual sentiment knowledge between different languages. The authors conduct their experiments on user comments from Facebook, tweets from Twitter, and multilingual product reviews from Amazon.