MuSES: Multilingual Sentiment Elicitation System for Social Media Data
IEEE Intelligent SystemsPublished 16 July 2013
Yusheng Xie, Zhengzhang Chen, Kunpeng Zhang, Yu Cheng, Daniel Honbo, Ankit Agrawal
Citations41
SJR quartileQ1
SJR score1.33
SNIP2.01
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TL;DR
A proposed label-free process transfers multilingual sentiment knowledge between different languages and defines a scoring function that measures the degree of a sentiment, instead of simply classifying a sentiment into binary polarities.
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.
Keywords
Computer Science
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