A social tag-based dimensional model of emotionsbuilding cross-domain folksonomies
- Fernández Tobías, Ignacio
- Cantador, Iván
- Plaza Morales, Laura
ISSN: 1135-5948
Year of publication: 2013
Issue: 51
Pages: 195-202
Type: Article
More publications in: Procesamiento del lenguaje natural
Abstract
We present an emotion computational model based on social tags. The model is built upon an automatically generated lexicon that describes emotions by means of synonym and antonym terms, and that is linked to multiple domain- specic emotion folksonomies extracted from entertainment social tagging systems. Using these cross-domain folksonomies, we develop a number of methods that au- tomatically transform tag-based item proles into emotion-oriented item proles, which may be exploited by adaptation and personalization systems. To validate our model, we show that its representation of a number of core emotions is in accordance with the well known psychological circumplex model of aect. We also report results from a user study that show a high precision of our methods to infer the emotions evoked by items in the movie and music domains.
Bibliographic References
- Baccianella, S., A. Esuli, and F. Sebastiani. 2010. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the 7th Conference on Language Resources and Evaluation (LREC’10).
- Baeza-Yates, R. A. and B.A. Ribeiro-Neto. 2011. Modern Information Retrieval - The Concepts and Technology behind Search. Pearson Education.
- Buckley, C. and E. Voorhees, 2005. TREC: Experiment and Evaluation in Information Retrieval, chapter Retrieval System Evaluation. MIT Press.
- Cantador, I., P. Brusilovsky, and T. Kuflik. 2011. Second workshop on information heterogeneity and fusion in recommender systems. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11), pages 387–388.
- Cantador, I., I. Konstas, and J. Jose. 2011. Categorising social tags to improve folksonomy-based recommendations. Journal of Web Semantics, 9(1):1–15.
- Carrillo-De-Albornoz, J., L. Plaza, and P. Gerv´as. 2010. A hybrid approach to emotional sentence polarity and intensity classification. pages 153–161.
- Carrillo-De-Albornoz, J., L. Plaza, and P. Gervás. 2012. Sentisense: An easily scalable concept-based affective lexicon for sentiment analysis. In Proceedings of the 8th Conference on Language Resources and Evaluation (LREC’12).
- De Choudhury, M., Counts S. and M. Gamon. 2012. Not all moods are created equal! exploring human emotional states in social media. In Proceedings of the 6th International AAAI Conference on Weblogs and Social Media.
- Hastings, J., W. Ceusters, B. Smith, and K. Mulligan. 2011. The emotion ontology: Enabling interdisciplinary research in the affective sciences. In Proceedings of the 7th International and Interdisciplinary Conference on Modeling and Using Context (Context’11).
- James, W. 1984. What is emotion? Mind,9:188–205.
- Meyers, O. C. 2007. A mood-based music classification and exploration system. Master’s thesis, School of Architecture and Planning, MIT.
- Picard, R. W. 1995. Affective computing. Technical Report 321, MIT Media Laboratory, Perceptual Computing Section.
- Russell, J.A. 1980. A circumplex model of affect. Journal of Personality and Social Psychology, 39(6):1161–1178.
- Scherer, K. R., A. Shorr, and T. (Eds.) Johnstone. 2001. Appraisal Processes in Emotion: Theory, Methods, Research.
- Winoto, P. and T. Ya Tang. 2010. The role of user mood in movie recommendations. Expert Systems with Applications, 37(8):6086–6092.
- Zentner, M., D. Grandjean, and K. Scherer. 2008. Emotions evoked by the sound of music: Characterization, classification, and measurement. Emotion, 8:494–521.