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Taking sides: user classification for informal online political discourse

Taking sides: user classification for informal online political discourse Purpose – To evaluate and extend, existing natural language processing techniques into the domain of informal online political discussions. Design/methodology/approach – A database of postings from a US political discussion site was collected, along with self‐reported political orientation data for the users. A variety of sentiment analysis, text classification, and social network analysis methods were applied to the postings and evaluated against the users' self‐descriptions. Findings – Purely text‐based methods performed poorly, but could be improved using techniques which took into account the users' position in the online community. Research limitations/implications – The techniques we applied here are fairly simple, and more sophisticated learning algorithms may yield better results for text‐based classification. Practical implications – This work suggests that social network analysis is an important tool for performing natural language processing tasks with informal web texts. Originality/value – This research extends sentiment analysis to a new subject domain (US politics) and a new text genre (informal online discusssions). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Internet Research Emerald Publishing

Taking sides: user classification for informal online political discourse

Internet Research , Volume 18 (2): 14 – Apr 4, 2008

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Publisher
Emerald Publishing
Copyright
Copyright © 2008 Emerald Group Publishing Limited. All rights reserved.
ISSN
1066-2243
DOI
10.1108/10662240810862239
Publisher site
See Article on Publisher Site

Abstract

Purpose – To evaluate and extend, existing natural language processing techniques into the domain of informal online political discussions. Design/methodology/approach – A database of postings from a US political discussion site was collected, along with self‐reported political orientation data for the users. A variety of sentiment analysis, text classification, and social network analysis methods were applied to the postings and evaluated against the users' self‐descriptions. Findings – Purely text‐based methods performed poorly, but could be improved using techniques which took into account the users' position in the online community. Research limitations/implications – The techniques we applied here are fairly simple, and more sophisticated learning algorithms may yield better results for text‐based classification. Practical implications – This work suggests that social network analysis is an important tool for performing natural language processing tasks with informal web texts. Originality/value – This research extends sentiment analysis to a new subject domain (US politics) and a new text genre (informal online discusssions).

Journal

Internet ResearchEmerald Publishing

Published: Apr 4, 2008

Keywords: Politics; Databases; Online operations; United States of America

References