A social tag-based dimensional model of emotions: building cross-domain folksonomies

• Autores: Ignacio Fernández Tobías, Iván Cantador , Laura Plaza Morales
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 51, 2013, págs. 195-202
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen
  • español

    En este trabajo se presenta un modelo dimensional de emociones basado en etiquetas sociales. El modelo se construye sobre un lexico generado automatica- mente que caracteriza emociones por medio de terminos sinonimos y antonimos.

    Este lexico se enlaza con diversas folcsonomas emocionales espec cas de dominio.

    Se propone una serie de metodos para transformar per les de objetos basados en etiquetas sociales en per les emocionales. El objetivo de estos per les es su uso por parte de sistemas adaptativos y de personalizacion que permitan recuperar o re- comendar contenidos en funcion del estado de animo del usuario. Para validar el modelo, se muestra que la representacion de un conjunto de emociones basicas se corresponde con la del aceptado modelo de Russell. Tambien se reportan resultados de un estudio de usuario que demuestran una alta precision de los metodos propues- tos para inferir emociones evocadas por objetos en los dominios del cine y la musica.

  • English

    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- speci c 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 pro les into emotion-oriented item pro les, 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 a ect. 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.

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