Abstract
In this paper 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-specific emotion folksonomies extracted from entertainment social tagging systems. Using these cross-domain folksonomies, we develop a number of methods that automatically transform tag-based item profiles into emotion-oriented item profiles. To validate our model we 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, and results from an offline evaluation that show accuracy improvements on model-based recommender systems that incorporate the extracted item emotional information.
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Aslam, J.A., Yilmaz, E.: A Geometric Interpretation and Analysis of R-precision. In: 14th ACM Conference on Information and Knowledge Management, pp. 664–671 (2005)
Baldoni, M., Baroglio, C., Patti, V., Rena, P.: From Tags to Emotions: Ontology-driven Sentiment Analysis in the Social Semantic Web. Intell. Artificiale 6(1), 41–54 (2012)
Breazeal, C.: Emotion and Sociable Humanoid Robots. International Journal of Human-Computer Studies 59, 119–155 (2003)
Cantador, I., Bellogín, A., Fernández-Tobías, I., López-Hernández, S.: Semantic Contextualisation of Social Tag-Based Profiles and Item Recommendations. In: Huemer, C., Setzer, T. (eds.) EC-Web 2011. LNBIP, vol. 85, pp. 101–113. Springer, Heidelberg (2011)
Cantador, I., Brusilovsky, P., Kuflik, T.: Second Workshop on Information Heterogeneity and Fusion in Recommender Systems. In: 5th ACM Conference on Recommender Systems, pp. 387–388 (2011)
Carrillo de Albornoz, J., Plaza, L., Gervás, P.: A Hybrid Approach to Emotional Sentence Polarity and Intensity Classification. In: 14th Intl. Conference on Computational Natural Language Learning, pp. 153–161 (2010)
Cassell, J.: Nudge Nudge Wink Wink: Elements of Face-to-Face Conversation for Embodied Conversational Agents. In: Embodied Conversational Agents, pp. 1–27 (2003)
Ekman, P., Davidson, R.J. (eds.): The Nature of Emotions: Fundamental Questions. Oxford University Press (1994)
Feng, Y., Zhuang, Y., Pan, Y.: Popular Music Retrieval by Detecting Mood. In: 6th ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 375–376 (2003)
Hastings, J., Ceusters, W., Smith, B., Mulligan, K.: The Emotion Ontology: Enabling Interdisciplinary Research in the Affective Sciences. In: Beigl, M., Christiansen, H., Roth-Berghofer, T.R., Kofod-Petersen, A., Coventry, K.R., Schmidtke, H.R. (eds.) CONTEXT 2011. LNCS, vol. 6967, pp. 119–123. Springer, Heidelberg (2011)
Hume, D.: Emotions and Moods. In: Robbins, S.P., Judge, T.A. (eds.) Organizational Behavior, pp. 258–297
Kaminskas, M., Ricci, F.: Location-Adapted Music Recommendation Using Tags. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 183–194. Springer, Heidelberg (2011)
Meyers, O.C.: A Mood-based Music Classification and Exploration System. MSc Thesis. School of Architecture and Planning, MIT (2007)
Roberts, K., Roach, M.A., Johnson, J., Guthrie, J., Harabagiu, S.M.: EmpaTweet: Annotating and Detecting Emotions on Twitter. In: 8th International Conference on Language Resources and Evaluation, pp. 3806–3813 (2012)
Russell, J.A.: A Circumplex Model of Affect. Journal of Personality and Social Psychology 39(6), 1161–1178 (1980)
Shi, Y., Larson, M., Hanjalic, A.: Mining Mood-specific Movie Similarity with Matrix Factorization for Context-aware Recommendation. In: Workshop on Context-Aware Movie Recommendation, pp. 34–40 (2010)
Shi, Y., Larson, M., Hanjalic, A.: Tags as Bridges between Domains: Improving Recommendation with Tag-Induced Cross-Domain Collaborative Filtering. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 305–316. Springer, Heidelberg (2011)
Winoto, P., Ya Tang, T.: The Role of User Mood in Movie Recommendations. Expert Systems with Applications 37(8), 6086–6092 (2010)
Zentner, M., Grandjean, D., Scherer, K.: Emotions Evoked by the Sound of Music: Characterization, Classification, and Measurement. Emotion 8, 494–521 (2008)
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Fernández-Tobías, I., Cantador, I., Plaza, L. (2013). An Emotion Dimensional Model Based on Social Tags: Crossing Folksonomies and Enhancing Recommendations. In: Huemer, C., Lops, P. (eds) E-Commerce and Web Technologies. EC-Web 2013. Lecture Notes in Business Information Processing, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39878-0_9
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DOI: https://doi.org/10.1007/978-3-642-39878-0_9
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