Entities' sentiment relevance

Zvi Ben-Ami, Ronen Feldman, Binyamin Rosenfeld

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Sentiment relevance detection problems occur when there is a sentiment expression in a text, and there is the question of whether or not the expression is related to a given entity or, more generally, to a given situation. The paper discusses variants of the problem, and shows that it is distinct from other somewhat similar problems occurring in the field of sentiment analysis and opinion mining. We experimentally demonstrate that using the information about relevancy significantly affects the final sentiment evaluation of the entities. We then compare a set of different algorithms for solving the relevance detection problem. The most accurate results are achieved by algorithms that use certain document-level information about the target entities. We show that this information can be accurately extracted using supervised classification methods.

Original languageAmerican English
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages87-92
Number of pages6
ISBN (Print)9781937284732
DOIs
StatePublished - 2014
Event52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Baltimore, MD, United States
Duration: 22 Jun 201427 Jun 2014

Publication series

Name52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference
Volume2

Conference

Conference52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014
Country/TerritoryUnited States
CityBaltimore, MD
Period22/06/1427/06/14

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