Combining probabilistic language models for aspect-based sentiment retrieval

Authors:
Lisette García-Moya;Henry Anaya-Sánchez;Rafael Berlanga-Llavori
Affiliations:
Universitat Jaume I, Spain;Universitat Jaume I, Spain;Universitat Jaume I, Spain
Venue:
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Year:
2012

Citing 2
Cited 0

Extracting and ranking product features in opinion documents

COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Aspect ranking: identifying important product aspects from online consumer reviews

HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1

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Abstract

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.