Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/107837
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
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 1611-3349 |
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 Responsibility: | 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 |
Published version: | http://dx.doi.org/10.1007/978-3-319-26190-4_33 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
RA_hdl_107837.pdf Restricted Access | Restricted Access | 650.98 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.