Opinion Bias Detection Based on Social Opinions for Twitter


A-Rong Kwon, Kyung-Soon Lee, Journal of Information Processing Systems Vol. 9, No. 4, pp. 538-547, Dec. 2013  

10.3745/JIPS.2013.9.4.538
Keywords: Social opinion, Personal opinion, Bias detection, Sentiment, Target
Fulltext:

Abstract

In this paper, we propose a bias detection method that is based on personal and social opinions that express contrasting views on competing topics on Twitter. We used unsupervised polarity classification is conducted for learning social opinions on targets. The tf-idf algorithm is applied to extract targets to reflect sentiments and features of tweets. Our method addresses there being a lack of a sentiment lexicon when learning social opinions. To evaluate the effectiveness of our method, experiments were conducted on four issues using Twitter test collection. The proposed method achieved significant improvements over the baselines.


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Cite this article
[APA Style]
A-Rong Kwon and Kyung-Soon Lee (2013). Opinion Bias Detection Based on Social Opinions for Twitter. Journal of Information Processing Systems, 9(4), 538-547. DOI: 10.3745/JIPS.2013.9.4.538.

[IEEE Style]
A. Kwon and K. Lee, "Opinion Bias Detection Based on Social Opinions for Twitter," Journal of Information Processing Systems, vol. 9, no. 4, pp. 538-547, 2013. DOI: 10.3745/JIPS.2013.9.4.538.

[ACM Style]
A-Rong Kwon and Kyung-Soon Lee. 2013. Opinion Bias Detection Based on Social Opinions for Twitter. Journal of Information Processing Systems, 9, 4, (2013), 538-547. DOI: 10.3745/JIPS.2013.9.4.538.