Sentence-level contextual opinion retrieval
Document Type
Conference Proceeding
Publication Title
Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011
Abstract
Existing opinion retrieval techniques do not provide context-dependent relevant results. Most of the approaches used by state-of-the-art techniques are based on frequency of query terms, such that all documents containing query terms are retrieved, regardless of contextual relevance to the intent of the human seeking the opinion. However, in a particular opinionated document, words could occur in different contexts, yet meet the frequency attached to a certain opinion threshold, thus explicitly creating a bias in overall opinion retrieved. In this paper we propose a sentence-level contextual model for opinion retrieval using grammatical tree derivations and approval voting mechanism. Model evaluation performed between our contextual model, BM25, and language model shows that the model can be effective for contextual opinion retrieval such as faceted opinion retrieval. © 2011 ACM.
First Page
403
Last Page
407
DOI
10.1145/1963192.1963351
Publication Date
4-29-2011
Recommended Citation
Orimaye, Sylvester Olubolu, "Sentence-level contextual opinion retrieval" (2011). Global Population Health Faculty Publications. 33.
https://doi.org/10.1145/1963192.1963351
https://collections.uhsp.edu/global-population-health_pubs/33