Dependency tree-based rules for concept-level aspect-based sentiment analysis

Soujanya Poria, Nir Ofek, Alexander Gelbukh, Amir Hussain, Lior Rokach

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

22 Scopus citations

Abstract

Over the last few years, the way people express their opinions has changed dramatically with the progress of social networks, web communities, blogs, wikis, and other online collaborative media. Now, people buy a product and express their opinion in social media so that other people can acquire knowledge about that product before they proceed to buy it. On the other hand, for the companies it has become necessary to keep track of the public opinions on their products to achieve customer satisfaction. Therefore, nowadays opinion mining is a routine task for every company for developing a widely acceptable product or providing satisfactory service. Concept-based opinion mining is a new area of research. The key parts of this research involve extraction of concepts from the text, determining product aspects, and identifying sentiment associated with these aspects. In this paper, we address each one of these tasks using a novel approach that takes text as input and use dependency parse tree-based rules to extract concepts and aspects and identify the associated sentiment. On the benchmark datasets, our method outperforms all existing state-of-the-art systems.

Original languageEnglish
Title of host publicationSemantic Web Evaluation Challenge - SemWebEval 2014 at ESWC 2014, Revised Selected Papers
EditorsTommaso Di Noia, Valentina Presutti, Diego Reforgiato Recupero, Iván Cantador, Christoph Lange, Christoph Lange, Anna Tordai, Christoph Lange, Milan Stankovic, Erik Cambria, Angelo Di Iorio
PublisherSpringer Verlag
Pages41-47
Number of pages7
ISBN (Electronic)9783319120232
DOIs
StatePublished - 1 Jan 2014

Publication series

NameCommunications in Computer and Information Science
Volume475
ISSN (Print)1865-0929

Keywords

  • Sentiment Analysis
  • Concept Extraction
  • Online Collaborative Media
  • Implicit Aspects
  • Prepositional Relations

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics

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