Combining Probabilistic Language Models for Aspect-Based Sentiment Retrieval
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Otros documentos de la autoría: García Moya, Lisette; Anaya Sánchez, Henry; Berlanga Llavori, Rafael
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/54899
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INVESTIGACIONMetadatos
Título
Combining Probabilistic Language Models for Aspect-Based Sentiment RetrievalFecha de publicación
2012Editor
Springer Berlin HeidelbergISBN
978-3-642-28996-5ISSN
0302-9743; 1611-3349Cita bibliográfica
García Moya, Lisette; Anaya Sánchez, Henry, Berlanga Llavori,Rafael " Combining Probabilistic Language Models for Aspect-Based Sentiment Retrieval ". En: Advances in Information Retrieval– 34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012. Proceedings / Baeza-Yates, Ricardo [et al.] (Eds.). Berlin : Springer, 2012. (Lecture Notes in Computer Science; 7224) . ISBN 978-3-642-28996-5, pp. 561-564Tipo de documento
info:eu-repo/semantics/bookPartVersión de la editorial
http://link.springer.com/chapter/10.1007/978-3-642-28997-2_64#Palabras clave / Materias
Resumen
In this paper, we present a new methodology aimed at retrieving relevant product aspects from a collection of customer reviews, as well as the most salient sentiments expressed about them. Our proposal is both unsup ... [+]
In this paper, we present a new methodology aimed at retrieving relevant product aspects from a collection of customer reviews, as well as the most salient sentiments expressed about them. Our proposal is both unsupervised and domain independent, and does not relies on NLP techniques such as parsing or dependence analysis. In our experiments, the proposed method achieves good values of precision. It is also shown that our approach is capable of properly retrieving the relevant aspects and their sentiments even from individual reviews. [-]
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