Ontology-Enhanced Aspect-Based Sentiment Analysis

  • Kim SchoutenEmail author
  • Flavius Frasincar
  • Franciska de Jong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10360)


With many people freely expressing their opinions and feelings on the Web, much research has gone into modeling and monetizing opinionated, and usually unstructured and textual, Web-based content. Aspect-based sentiment analysis aims to extract the fine-grained topics, or aspects, that people are talking about, together with the sentiment expressed on those aspects. This allows for a detailed analysis of the sentiment expressed in, for instance, product and service reviews. In this work we focus on knowledge-driven solutions that aim to complement standard machine learning methods. By encoding common domain knowledge into a knowledge repository, or ontology, we are able to exploit this information to improve classification performance for both aspect detection and aspect sentiment analysis. For aspect detection, the ontology-enhanced method needs only 20% of the training data to achieve results comparable with a standard bag-of-words approach that uses all training data.



The authors of this paper are supported by the Dutch national program COMMIT.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kim Schouten
    • 1
    Email author
  • Flavius Frasincar
    • 1
  • Franciska de Jong
    • 1
  1. 1.Erasmus University RotterdamRotterdamThe Netherlands

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