Damping sentiment analysis in online communication: discussions, monologs and dialogs

Authors:
Mike Thelwall;Kevan Buckley;George Paltoglou;Marcin Skowron;David Garcia;Stephane Gobron;Junghyun Ahn;Arvid Kappas;Dennis Küster;Janusz A. Holyst
Affiliations:
Statistical Cybermetrics Research Group, University of Wolverhampton, Wolverhampton, UK;Statistical Cybermetrics Research Group, University of Wolverhampton, Wolverhampton, UK;Statistical Cybermetrics Research Group, University of Wolverhampton, Wolverhampton, UK;Austrian Research Institute for Artificial Intelligence, Vienna, Austria;Chair of Systems Design, ETH Zurich, Zurich, Switzerland;Information and Communication Systems Institute (ISIC), HE-Arc, HES-SO, Switzerland;SCI IC RB Group, Ecole polytechnique fédérale de Lausanne EPFL, Switzerland;School of Humanities and Social Sciences, Jacobs University Bremen, Bremen, Germany;School of Humanities and Social Sciences, Jacobs University Bremen, Bremen, Germany;Center of Excellence for Complex Systems Research, Faculty of Physics, Warsaw University of Technology, Warsaw, Poland
Venue:
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Year:
2013

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Abstract

Sentiment analysis programs are now sometimes used to detect patterns of sentiment use over time in online communication and to help automated systems interact better with users. Nevertheless, it seems that no previous published study has assessed whether the position of individual texts within on-going communication can be exploited to help detect their sentiments. This article assesses apparent sentiment anomalies in on-going communication --- texts assigned significantly different sentiment strength to the average of previous texts --- to see whether their classification can be improved. The results suggest that a damping procedure to reduce sudden large changes in sentiment can improve classification accuracy but that the optimal procedure will depend on the type of texts processed.