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. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
García-Moya, L., Anaya-Sánchez, H., & Berlanga-Llavori, R. (2012). Combining probabilistic language models for aspect-based sentiment retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7224 LNCS, pp. 561–564). https://doi.org/10.1007/978-3-642-28997-2_64
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