Polarity Detection of Online Reviews Using Sentiment Concepts: NCU IISR Team at ESWC-14 Challenge on Concept-Level Sentiment Analysis

  • Jay Kuan-Chieh Chung
  • Chi-En Wu
  • Richard Tzong-Han Tsai
Conference paper

DOI: 10.1007/978-3-319-12024-9_7

Part of the Communications in Computer and Information Science book series (CCIS, volume 475)
Cite this paper as:
Chung J.KC., Wu CE., Tsai R.TH. (2014) Polarity Detection of Online Reviews Using Sentiment Concepts: NCU IISR Team at ESWC-14 Challenge on Concept-Level Sentiment Analysis. In: Presutti V. et al. (eds) Semantic Web Evaluation Challenge. SemWebEval 2014. Communications in Computer and Information Science, vol 475. Springer, Cham

Abstract

In this paper, we present our system that participated in the Polarity Detection task, the elementary task in the ESWC-14 Challenge on Concept-Level Sentiment Analysis. In addition to traditional Bag-of-Words features, we also employ state-of-the-art Sentic API to extract concepts from documents to generate Bag-of-Sentiment-Concepts features. Our previous work SentiConceptNet serves as the reference concept-based sentiment knowledge base for concept-level sentiment analysis. Experimental results on our development set show that adding Bag-of-Sentiment-Concepts can improve the accuracy by 1.3 %, indicating the benefit of concept-level sentiment analysis. Our demo website is located at http://140.115.51.136:5000.

Keywords

Concept-level sentiment analysis Sentiment concepts Polarity detection of online reviews 

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jay Kuan-Chieh Chung
    • 3
  • Chi-En Wu
    • 2
  • Richard Tzong-Han Tsai
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Central UniversityJhongliTaiwan, R.O.C.
  2. 2.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiTaiwan, R.O.C.
  3. 3.Department of Computer Science and EngineeringYuan Ze UniversityJhongliTaiwan, R.O.C.

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