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A Fine-Grained Ontology-Based Sentiment Aggregation Approach

  • Monireh Alsadat MirtalaieEmail author
  • Omar Khadeer Hussain
  • Elizabeth Chang
  • Farookh Khadeer Hussain
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 772)

Abstract

Sentiment analysis techniques are widely used to capture the voice of customers about different products/services. Aspect or feature-based sentiment detection tools as one of the sentiment analyses’ types are developed to find the customers’ opinions about various features of a product. However, as a product may contain many features, presenting the final obtained results to the users is a challenge. Even though this issue is addressed in the literature by developing different sentiment aggregation methods, their results are mostly presented at the basic-level features of a product. This may cause in losing customers’ opinion about at minor sub-features. However, as the performance of a basic feature is dependent on those of its different sub-features, we propose an approach which aggregates the extracted results at a fine-grained level features using a product ontology tree. We interpret the polarity of each feature as a satisfaction score which can help managers in investigating the weaknesses of their products even at minor levels in a more informed way.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Monireh Alsadat Mirtalaie
    • 1
    Email author
  • Omar Khadeer Hussain
    • 1
  • Elizabeth Chang
    • 1
  • Farookh Khadeer Hussain
    • 2
  1. 1.University of New South WalesCanberraAustralia
  2. 2.School of SoftwareUniversity of TechnologySydneyAustralia

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