Tower of babel: a crowdsourcing game building sentiment lexicons for resource-scarce languages

• Main Authors: HONG, Yoonsung, KWAK, Haewoon, BAEK, Youngmin, MOON, Sue.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: World Wide Web; Distributed knowledge acquisition; Lexicon construction; Sentiment labeling; online games; Data Storage Systems; Programming Languages and Compilers
• Online Access: https://ink.library.smu.edu.sg/sis_research/6096; https://ink.library.smu.edu.sg/context/sis_research/article/7099/viewcontent/Tower_of_Babel.pdf
• Institution: Singapore Management University

With the growing amount of textual data produced by online social media today, the demands for sentiment analysis are also rapidly increasing; and, this is true for worldwide. However, non-English languages often lack sentiment lexicons, a core resource in performing sentiment analysis. Our solution, Tower of Babel (ToB), is a language-independent sentiment-lexicon-generating crowdsourcing game. We conducted an experiment with 135 participants to explore the difference between our solution and a conventional manual annotation method. We evaluated ToB in terms of effectiveness, efficiency, and satisfactions. Based on the result of the evaluation, we conclude that sentiment classification via ToB is accurate, productive and enjoyable.