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Taking sides: user classification for informal online political discourse

Robert Malouf (Department of Linguistics and Asian/Middle Eastern Languages, San Diego State University, San Diego, California, USA)
Tony Mullen (Department of Computer Science, Tsuda College, Tokyo, Japan)

Internet Research

ISSN: 1066-2243

Publication date: 4 April 2008

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).

Keywords

  • Politics
  • Databases
  • Online operations
  • United States of America

Citation

Malouf, R. and Mullen, T. (2008), "Taking sides: user classification for informal online political discourse", Internet Research, Vol. 18 No. 2, pp. 177-190. https://doi.org/10.1108/10662240810862239

Download as .RIS

Publisher

:

Emerald Group Publishing Limited

Copyright © 2008, Emerald Group Publishing Limited

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