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Grouping Product Aspects from Short Texts Using Multiple Classifiers Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Classifying Perspectives on Twitter: Immediate Observation, Affection, and Speculation

Authors : Yihong Zhang, Claudia Szabo, Quan Z. Sheng, Xiu Susie Fang

Published in: Web Information Systems Engineering – WISE 2015

Publisher: Springer International Publishing

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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.

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previous chapter A Hybrid Feature Selection Method for Predicting User Influence on Twitter
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Footnotes
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Metadata
Title
Classifying Perspectives on Twitter: Immediate Observation, Affection, and Speculation
Authors
Yihong Zhang
Claudia Szabo
Quan Z. Sheng
Xiu Susie Fang
Copyright Year
2015
Book
Web Information Systems Engineering – WISE 2015

Print ISBN: 978-3-319-26189-8

Electronic ISBN: 978-3-319-26190-4

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26190-4_33

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