Combining Probabilistic Language Models for Aspect-Based Sentiment Retrieval
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.
KeywordsSentiment analysis aspect retrieval language models
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- 1.Carenini, G., Ng, R., Pauls, A.: Multi-document summarization of evaluative text. In: Proc. of EACL 2006, pp. 305–312 (2006)Google Scholar
- 2.Dillon, J., Mao, Y., Lebanon, G., Zhang, J.: Statistical Translation, Heat Kernels, and Expected Distance. In: Proc. of the 23rd Conference on Uncertainty in Artificial Intelligence (2007)Google Scholar
- 3.Yu, J., Zha, Z., Wang, M., Chua, T.: Aspect ranking: identifying important product aspects from online consumer reviews. In: Proc. of ACL 2011, pp. 1496–1505 (2011)Google Scholar
- 4.Zhang, L., Liu, B., Lim, S.H., O’Brien-Strain, E.: Extracting and ranking product features in opinion documents. In: Proc. of COLING 2010, pp. 1462–1470 (2010)Google Scholar