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Quantising opinions for political tweets analysis

He, Yulan; Saif, Hassan; Wei, Zhongyu and Wong, Kam-Fai (2012). Quantising opinions for political tweets analysis. In: LREC 2012, Eighth International Conference on Language Resources and Evaluation, 21-27 May 2012, Istanbul, Turkey.

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There have been increasing interests in recent years in analyzing tweet messages relevant to political events so as to understand public opinions towards certain political issues. We analyzed tweet messages crawled during the eight weeks leading to the UK General Election in May 2010 and found that activities at Twitter is not necessarily a good predictor of popularity of political parties. We then proceed to propose a statistical model for sentiment detection with side information such as emoticons and hash tags implying tweet polarities being incorporated. Our results show that sentiment analysis based on a simple keyword matching against a sentiment lexicon or a supervised classifier trained with distant supervision does not correlate well with the actual election results. However, using our proposed statistical model for sentiment analysis, we were able to map the public opinion in Twitter with the actual offline sentiment in real world.

Item Type: Conference or Workshop Item
Copyright Holders: 2012 The Authors
Project Funding Details:
Funded Project NameProject IDFunding Body
ROBUSTGrant number 257859EC-FP7
Not SetNot SetRoyal Academy of Engineering, UK
Keywords: political tweets analysis; sentiment analysis; joint sentiment-topic (JST) model
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 40659
Depositing User: Kay Dave
Date Deposited: 05 Aug 2014 14:41
Last Modified: 07 Dec 2018 10:59
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