Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2013

Abstract

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.

Keywords

World Wide Web, Distributed knowledge acquisition, Lexicon construction, Sentiment labeling, online games

Discipline

Data Storage Systems | Programming Languages and Compilers

Research Areas

Data Science and Engineering

Publication

Proceedings of the 22nd International Conference on World Wide Web, WWW 2013, Rio de Janeiro, Brazil, May 13-17

First Page

549

Last Page

556

ISBN

9781450320382

Identifier

10.1145/2487788.2487993

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/2487788.2487993

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