This Article 
 Bibliographic References 
 Add to: 
Seeing Stars of Valence and Arousal in Blog Posts
Jan.-March 2013 (vol. 4 no. 1)
pp. 116-123
Georgios Paltoglou, University of Wolverhampton, Wolverhampton
Michael Thelwall, University of Wolverhampton, Wolverhampton
Sentiment analysis is a growing field of research, driven by both commercial applications and academic interest. In this paper, we explore multiclass classification of diary-like blog posts for the sentiment dimensions of valence and arousal, where the aim of the task is to predict the level of valence and arousal of a post on a ordinal five-level scale, from very negative/low to very positive/high, respectively. We show how to map discrete affective states into ordinal scales in these two dimensions, based on the psychological model of Russell's circumplex model of affect and label a previously available corpus with multidimensional, real-valued annotations. Experimental results using regression and one-versus-all approaches of support vector machine classifiers show that although the latter approach provides better exact ordinal class prediction accuracy, regression techniques tend to make smaller scale errors.
Index Terms:
Mood,Sentiment analysis,Data mining,Algorithm design and analysis,Predictive models,sentiment analysis,Mood,Sentiment analysis,Data mining,Algorithm design and analysis,Predictive models,affect detection,Mining methods and algorithms
Georgios Paltoglou, Michael Thelwall, "Seeing Stars of Valence and Arousal in Blog Posts," IEEE Transactions on Affective Computing, vol. 4, no. 1, pp. 116-123, Jan.-March 2013, doi:10.1109/T-AFFC.2012.36
Usage of this product signifies your acceptance of the Terms of Use.