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Article Information:
Title:
Taking sides: user classification for informal online political discourse
Author(s):
Robert Malouf, Tony Mullen
Journal:
Year:
2008
Volume:
Issue:
Page:
177 - 190
DOI:
10.1108/10662240810862239
Publisher:
Emerald Group Publishing Limited
Acknowledgements:
This work is supported by KAKENHI 18700158, Grant-in-Aid for Young Scientists (B) provided by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan.
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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:
Databases,
Online operations,
Politics,
United States of America
Article Type:
Research paper
References:
Article URL:
www.emeraldinsight.com/10.1108/10662240810862239