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Predicting Emotional Responses to Long Informal Text
Jan.-March 2013 (vol. 4 no. 1)
pp. 106-115
Georgios Paltoglou, University of Wolverhampton, Wolverhampton
Mathias Theunis, Jacobs University Bremen, Bremen
Arvid Kappas, Jacobs University Bremen, Bremen
Mike Thelwall, University of Wolverhampton, Wolverhampton
Most sentiment analysis approaches deal with binary or ordinal prediction of affective states (e.g., positive versus negative) on review-related content from the perspective of the author. The present work focuses on predicting the emotional responses of online communication in nonreview social media on a real-valued scale on the two affective dimensions of valence and arousal. For this, a new dataset is introduced, together with a detailed description of the process that was followed to create it. Important phenomena such as correlations between different affective dimensions and intercoder agreement are thoroughly discussed and analyzed. Various methodologies for automatically predicting those states are also presented and evaluated. The results show that the prediction of intricate emotional states is possible, obtaining at best a correlation of 0.89 for valence and 0.42 for arousal with the human assigned assessments.
Index Terms:
Human factors,Psychology,Sentiment analysis ,Predictive models,ANEW,Human factors,Psychology,Sentiment analysis,Predictive models,human annotation,Sentiment analysis,valence,arousal
Georgios Paltoglou, Mathias Theunis, Arvid Kappas, Mike Thelwall, "Predicting Emotional Responses to Long Informal Text," IEEE Transactions on Affective Computing, vol. 4, no. 1, pp. 106-115, Jan.-March 2013, doi:10.1109/T-AFFC.2012.26
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