Tower of babel: a crowdsourcing game building sentiment lexicons for resource-scarce languages
WWW (Companion Volume)(2013)
摘要
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.
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关键词
crowdsourcing game building sentiment,sentiment lexicon,conventional manual annotation method,non-english language,textual data,core resource,resource-scarce language,crowdsourcing game,online social media,sentiment classification,sentiment analysis,world wide web
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