Automatic vs. crowdsourced sentiment analysis

Ria Mae Borromeo, Motomichi Toyama

研究成果: Conference contribution

16 被引用数 (Scopus)

抄録

Due to the amount of work needed in manual sentiment analysis of written texts, techniques in automatic sentiment analysis have been widely studied. However, compared to manual sentiment analysis, the accuracy of automatic systems range only from low to medium. In this study, we solve a sentiment analysis problem by crowdsourcing. Crowdsourcing is a problem solving approach that uses the cognitive power of people to achieve specific computational goals. It is implemented through an online platform, which can either be paid or volunteer-based. We deploy crowdsourcing applications in paid and volunteer-based platforms to classify teaching evaluation comments from students. We present a comparison of the results produced by crowdsourcing, manual sentiment analysis, and an existing automatic sentiment analysis system. Our findings show that the crowdsourced sentiment analysis in both paid and volunteer-based platforms are considerably more accurate than the automatic sentiment analysis algorithm but still fail to achieve high accuracy compared to the manual method. To improve accuracy, the effect of increasing the size of the crowd could be explored in the future.

本文言語English
ホスト出版物のタイトルACM International Conference Proceeding Series
編集者Bipin C. Desai, Motomichi Toyama
出版社Association for Computing Machinery
ページ90-95
ページ数6
CONFCODENUMBER
ISBN(電子版)9781450334143
DOI
出版ステータスPublished - 2015 7月 13
イベント19th International Database Engineering and Applications Symposium, IDEAS 2015 - Yokohama, Japan
継続期間: 2015 7月 132015 7月 15

出版物シリーズ

名前ACM International Conference Proceeding Series
番号CONFCODENUMBER
0

Other

Other19th International Database Engineering and Applications Symposium, IDEAS 2015
国/地域Japan
CityYokohama
Period15/7/1315/7/15

ASJC Scopus subject areas

  • ソフトウェア
  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ ネットワークおよび通信

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