Sentiment analysis on tweets for social events

Sentiment analysis or opinion mining is an important type of text analysis that aims to support decision making by extracting and analyzing opinion oriented text, identifying positive and negative opinions, and measuring how positively or negatively an entity (i.e., people, organization, event, loca...

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Bibliographic Details
Main Authors: Xujuan Zhou, Xiaohui Tao, Jianming Yong, Zhenyu Yang
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Educational institutions
Feature extraction
Nominations and elections
Semantics
Sentiment analysis
Social network
Text analysis
Tweets
Twitter
Online Access:Request full text
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Summary:Sentiment analysis or opinion mining is an important type of text analysis that aims to support decision making by extracting and analyzing opinion oriented text, identifying positive and negative opinions, and measuring how positively or negatively an entity (i.e., people, organization, event, location, product, topic, etc.) is regarded. As more and more users express their political and religious views on Twitter, tweets become valuable sources of people's opinions. Tweets data can be efficiently used to infer people's opinions for marketing or social studies. This paper proposes a Tweets Sentiment Analysis Model (TSAM) that can spot the societal interest and general people's opinions in regard to a social event. In this paper, Australian federal election 2010 event was taken as an example for sentiment analysis experiments. We are primarily interested in the sentiment of the specific political candidates, i.e., two primary minister candidates - Julia Gillard and Tony Abbot. Our experimental results demonstrate the effectiveness of the system.
DOI:10.1109/CSCWD.2013.6581022