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Enhanced Twitter sentiment analysis by using feature selection and combination

Yang, Ang, Zhang, Jun, Pan, Lei and Xiang, Yang 2015, Enhanced Twitter sentiment analysis by using feature selection and combination, in SOCIALSEC 2015: Proceedings of the International Symposium on Security and Privacy in Social Networks and Big Data, IEEE, Piscataway, N.J., pp. 52-57, doi: 10.1109/SocialSec2015.9.

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Title Enhanced Twitter sentiment analysis by using feature selection and combination
Author(s) Yang, Ang
Zhang, Jun
Pan, LeiORCID iD for Pan, Lei orcid.org/0000-0002-4691-8330
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Conference name Security and Privacy in Social Networks and Big Data. International Symposium (2015 : Hangzhou, China)
Conference location Hangzhou, China
Conference dates 16-18 Nov. 2015
Title of proceedings SOCIALSEC 2015: Proceedings of the International Symposium on Security and Privacy in Social Networks and Big Data
Publication date 2015
Start page 52
End page 57
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) sentiment analysis
Twitter
classification
supervized learning
performance
Summary Tweet sentiment analysis is an important research topic. An accurate and timely analysis report could give good indications on the general public's opinions. After reviewing the current research, we identify the need of effective and efficient methods to conduct tweet sentiment analysis. This paper aims to achieve a high level of performance for classifying tweets with sentiment information. We propose a feasible solution which improves the level of accuracy with good time efficiency. Specifically, we develop a novel feature combination scheme which utilizes the sentiment lexicons and the extracted tweet unigrams of high information gain. We evaluate the performance of six popular machine learning classifiers among which the Naive Bayes Multinomial (NBM) classifier achieves the accuracy rate of 84.60% and takes a few minutes to complete classifying thousands of tweets.
ISBN 9781467384209
Language eng
DOI 10.1109/SocialSec2015.9
Field of Research 080599 Distributed Computing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30080267

Document type: Conference Paper
Collection: School of Information Technology
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