Exploiting the Focus of the Document for Enhanced Entities' Sentiment Relevance Detection

Zvi Ben-Ami, Ronen Feldman, Benjamin Rosenfeld

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

3 Scopus citations

Abstract

A key question in sentiment analysis is whether sentiment ex-pressions, in a given text, are related to particular entities. This is an imperative question, since people are typically interested in sentiments on specific entities and not in the overall sentiment articulated in an article or a document. Sentiment relevance is aimed at addressing this precise problem. In this paper, we argue that exploiting information about the focus of the document on the entity of interest can significantly improve the task of detecting sentiment relevance and, hence, the final sentiment scores assigned for the entities. In order to assess the value of such information, we look at various methods for detecting sentiment relevance for entities. We consider both rule-based algorithms that rely on the entity's physical or syntactic proximity to the sentiment expressions as well as more sophisticated machine learning classification algorithms. We demonstrate that the focus of the document on the entities within it is, indeed, an important piece of information, which can be accurately learned with super-vised classification means. We, further, found that overall classification-based algorithms perform better than the deterministic ones in identifying sentiment relevance, with sequence-classification performing significantly better than direct classification.

Original languageAmerican English
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
EditorsXindong Wu, Alexander Tuzhilin, Hui Xiong, Jennifer G. Dy, Charu Aggarwal, Zhi-Hua Zhou, Peng Cui
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1284-1293
Number of pages10
ISBN (Electronic)9781467384926
DOIs
StatePublished - 29 Jan 2016
Event15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 - Atlantic City, United States
Duration: 14 Nov 201517 Nov 2015

Publication series

NameProceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

Conference

Conference15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Country/TerritoryUnited States
CityAtlantic City
Period14/11/1517/11/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • Document Type with Respect to Entity
  • Document-level Infor-mation
  • Entity-level Sentiment Analysis
  • Focus of the Document
  • Sentiment Analysis
  • Sentiment Relevance

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