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. Attached Files Name Description MIMEType Size Downloads Title Enhanced Twitter sentiment analysis by using feature selection and combination Author(s) Yang, Ang Zhang, Jun orcid.org/0000-0002-2189-7801 Pan, Lei orcid.org/0000-0002-4691-8330 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 Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 15 Dec 2015, 10:11:15 EST Wed, 16 Dec 2015, 04:00:33 EST Wed, 16 Dec 2015, 07:28:08 EST Mon, 06 Jun 2016, 17:56:18 EST Mon, 06 Jun 2016, 17:58:01 EST Tue, 07 Jun 2016, 06:06:30 EST Mon, 18 Jul 2016, 09:41:59 EST Mon, 18 Jul 2016, 09:42:49 EST Wed, 12 Oct 2016, 14:30:43 EST Wed, 09 Nov 2016, 11:11:29 EST Wed, 01 Mar 2017, 07:35:10 EST Filtered Full Citation counts:   Cited 6 times in TR Web of Science Cited 12 times in Scopus Search Google Scholar Access Statistics: 570 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 06 Jun 2016, 17:58:01 EST

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