Web mining for the mayoral election prediction in Taiwan
| DOI | https://doi.org/10.1108/AJIM-02-2017-0035 |
| Pages | 688-701 |
| Date | 20 November 2017 |
| Published date | 20 November 2017 |
| Author | Jia-Yen Huang |
| Subject Matter | Library & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management |
Web mining for the mayoral
election prediction in Taiwan
Jia-Yen Huang
Department of Information Management,
National Chin-Yi University of Technology, Taichung, Taiwan
Abstract
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.
Keywords Web mining, Socialmedia, Correspondence analysis, Chinese semanticanalysis, Election forecast,
Opinion mining
Paper type Research paper
Introduction
Nowadays, many people express their opinions on the internet, including opinions about
political issues. Not only governments need to know about their citizens’opinions, but also
most election campaigns and candidates are eager to know the voice of voters before the
election so they can adjust their election strategy promptly. Over the years,
election-related organizations have conducted polls forecasting, and the election results
are mostly consistent with the prediction. However, in the Taiwan local elections held on
November 29, 2014, the election results of many cities showed significant deviation from
predictions. For example, the pre-election polls of the mayoral candidate of the Taoyuan
city, Chih-Yang Wu, were 57 percent favorable, and those of his opponent, Wen-tsan
Cheng, were only 25 percent favorable. However, the polls’laggard turned out to be the
winner. Evidently, simply relying on the traditional polling method does not seem enough
to provide an adequate forecast.
With the advent of social media, such as Facebook, Plurk, and Twitter, users can express
their opinions anytime and anywhere. In general, young people are the main users of the
internet. As shown in Table I, 24 percent of the internet access population in Taiwan are
between the ages of 15 and 24, 29 percent of the internet access population are between
the ages of 25 and 34. Hence, using web reviews for election estimation mainly reflects the
opinions of young people.
Aslib Journal of Information
Management
Vol. 69 No. 6, 2017
pp. 688-701
© Emerald PublishingLimited
2050-3806
DOI 10.1108/AJIM-02-2017-0035
Received 3 February 2017
Revised 13 April 2017
29 June 2017
Accepted 30 August 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2050-3806.htm
688
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