Web mining for the mayoral election prediction in Taiwan

Purpose The prediction of pre-election polls is an issue of concern for both politicians and voters. The Taiwan nine-in-one election held in 2014 ended with jaw-dropping results; apparently, traditional polls did not work well. As a remedy to this problem, the purpose of this paper is to utilize the comments posted on social media to analyze civilians’ views on the two candidates for mayor of Taichung City, Chih-chiang Hu, and Chia-Lung Lin. Design/methodology/approach After conducting word segmentation and part-of-speech tagging for the collected reviews, this study constructs the opinion phrase extraction rules for identifying the opinion words associated with the attribute words. Next, this study classifies the attribute words into six municipal governance-related topics and calculates the opinion scores for each candidate. Finally, this study uses correspondence analysis to transform opinion information on the candidates into a graphical display to facilitate the interpretation of voters’ views. Findings The results show that the topics of candidates’ backgrounds and transport infrastructure were the two most critical factors for the election prediction. Based on the predication, Lin outscores Hu by 17.74 percent which is close to the real election results. Research limitations/implications This study proposes new rules for the extraction of Chinese opinion words associated with attribute words. Practical implications This study applies Chinese semantic analysis to assist in predicting election results and investigating the topics of concern to voters. Originality/value The proposed opinion phrase extraction rules for Chinese social media, as well as the election forecast process, can provide valuable references for political parties and candidates to plan better nomination and election strategies.

[1]  Christine B. Williams,et al.  What is a Social Network Worth? Facebook and Vote Share in the 2008 Presidential Primaries , 2008 .

[2]  Giuseppe Porro,et al.  Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France , 2013, New Media Soc..

[3]  Ben O'Loughlin,et al.  TRUST, CONFIDENCE, AND CREDIBILITY , 2011 .

[4]  Fabio Franch.  (Wisdom of the Crowds)2: 2010 UK Election Prediction with Social Media , 2013 .

[5]  Isabell M. Welpe,et al.  Election Forecasts With Twitter , 2011 .

[6]  Wenji Mao,et al.  Polarity Classification of Public Health Opinions in Chinese , 2008, ISI Workshops.

[7]  S. Albrecht,et al.  Weblog Campaigning in the German Bundestag Election 2005 , 2007 .

[8]  Marijke Breuning,et al.  21st century political science : a reference handbook , 2011 .

[9]  Martin Znidarsic,et al.  Monitoring the Twitter sentiment during the Bulgarian elections , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[10]  Panagiotis Takis Metaxas,et al.  How (Not) to Predict Elections , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[11]  Hsin-Hsi Chen,et al.  Predicting the Semantic Orientation of Terms in E-HowNet , 2012, Int. J. Comput. Linguistics Chin. Lang. Process..

[12]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[13]  Ben O'Loughlin,et al.  Social Media Analysis and Public Opinion: The 2010 UK General Election , 2015, J. Comput. Mediat. Commun..

[14]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[15]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[16]  Brendan T. O'Connor,et al.  From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series , 2010, ICWSM.

[17]  Jung-Hwan Bae,et al.  Analysis of Twitter for 2012 South Korea Presidential Election by Text Mining Techniques , 2013 .

[18]  Daniel A. Keim,et al.  Visual Sentiment Analysis of RSS News Feeds Featuring the US Presidential Election in 2008 , 2009 .

[19]  Andrew Chadwick,et al.  Text mining and social media: when quantitative meets qualitative, and software meets humans , 2015 .

[20]  Tekle Shanka,et al.  Factors Influencing International Students' Choice of an Education Destination–A Correspondence Analysis , 2006 .

[21]  J. Bollen,et al.  More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior , 2013, PloS one.

[22]  Daniel Gayo-Avello,et al.  A Meta-Analysis of State-of-the-Art Electoral Prediction From Twitter Data , 2012, ArXiv.

[23]  Lada A. Adamic,et al.  The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.

[24]  Rachel Gibson,et al.  140 Characters to Victory?: Using Twitter to Predict the UK 2015 General Election , 2015, ArXiv.

[25]  Xinying Xu,et al.  Hidden sentiment association in chinese web opinion mining , 2008, WWW.

[26]  Johan Bos,et al.  Predicting the 2011 Dutch Senate Election Results with Twitter , 2012 .