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Issue No.04 - July-Aug. (2014 vol.29)
pp: 34-42
Yusheng Xie , Northwestern University
Zhengzhang Chen , Northwestern University
Kunpeng Zhang , Northwestern University
Yu Cheng , Northwestern University
Daniel K. Honbo , Northwestern University
Ankit Agrawal , Northwestern University
Alok N. Choudhary , Northwestern University
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.
Internet, Media, Pragmatics, Electronic publishing, Sentiment analysis, Information retrieval, Facebook, Twitter, Social network servces, Computer interfaces, Identification,intelligent systems, information retrieval, sentiment analysis, multilingual sentiment identification, computer-mediated communication, Facebook, Twitter
Yusheng Xie, Zhengzhang Chen, Kunpeng Zhang, Yu Cheng, Daniel K. Honbo, Ankit Agrawal, Alok N. Choudhary, "MuSES: Multilingual Sentiment Elicitation System for Social Media Data", IEEE Intelligent Systems, vol.29, no. 4, pp. 34-42, July-Aug. 2014, doi:10.1109/MIS.2013.52
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