Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Sentiment retrieval using generative models
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A unified relevance model for opinion retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Ad hoc retrieval of documents with topical opinion
ECIR'07 Proceedings of the 29th European conference on IR research
Automatic construction of an opinion-term vocabulary for ad hoc retrieval
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Information Retrieval on the Blogosphere
Foundations and Trends in Information Retrieval
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We investigate the effectiveness of both the standard evaluation measures and the opinion component for topical opinion retrieval. We analyze how relevance is affected by opinions by perturbing relevance ranking by the outcomes of opinion-only classifiers built by Monte Carlo sampling. Topical opinion rankings are obtained by either re-ranking or filtering the documents of a first-pass retrieval of topic relevance. The proposed approach establishes the correlation between the accuracy and the precision of the classifier and the performance of the topical opinion retrieval. Among other results, it is possible to assess the effectiveness of the opinion component by comparing the effectiveness of the relevance baseline with the topical opinion ranking.