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Type: Conference paper
Title: Classifying perspectives on Twitter: immediate observation, affection, and speculation
Author: Zhang, Y.
Szabo, C.
Sheng, Q.
Fang, X.
Citation: Lecture Notes in Artificial Intelligence, 2015 / Wang, J., Cellary, W., Wang, D., Wang, H., Chen, S., Li, T., Zhang, Y. (ed./s), vol.9418, pp.493-507
Publisher: Springer
Issue Date: 2015
Series/Report no.: Lecture Notes in Computer Science
ISBN: 9783319261898
ISSN: 0302-9743
Conference Name: 16th International Conference on Web Information Systems Engineering (WISE) (1 Nov 2015 - 3 Nov 2015 : Miami, FL)
Editor: Wang, J.
Cellary, W.
Wang, D.
Wang, H.
Chen, S.
Li, T.
Zhang, Y.
Statement of
Yihong Zhang, Claudia Szabo, Quan Z. Sheng and Xiu Susie Fang
Abstract: Popular micro-blogging services such as Twitter enable users to effortlessly publish observations and thoughts about ongoing events. Such social sensing generates a very large pool of rich and up-to-date information. However, the large volume and a fast rate of posting make it very challenging to read through the posts and find out useful information in relevant tweets. In this paper, we propose an automated tweet classification approach that distinguishes three perspectives in which a Twitter user may compose messages, namely Immediate Observation, Affection, and Speculation. Using tweets made about the Ukraine Crisis in 2014, our experimental results show that, with the right choice of features and classifiers, we can generally obtain very satisfying results, with the classification precisions in many cases higher than 0.8. We show that the classification results can be used in event time and location detection, public sentiment analysis, and early rumor detection.
Keywords: Twitter; Social media; Data mining; Short message classification
Rights: © Springer International Publishing Switzerland 2015
DOI: 10.1007/978-3-319-26190-4_33
Appears in Collections:Aurora harvest 3
Computer Science publications

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