Damping sentiment analysis in online communication: Discussions, monologs and dialogs
Mike Thelwall, Kevan Buckley, George Paltoglou, Marcin Skowron, David Garcia, Stephane Gobron, Junghyun Ahn, Arvid Kappas, Dennis Kuster and A Janusz
In Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing (2013)
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 previouspublished study has assessed whether the position of individual texts within ongoing communication can be exploited to help detect their sentiments. This article assesses apparent sentiment anomalies in on - going communication – textsassigned significantly different sentiment strength to the average of previoustexts – to see whether their classification can be improved. The results suggestthat a damping procedure to reduce sudden large changes in sentiment can improve classification accuracy but that the optimal procedure will depend on thetype of texts processed.
