New Ontological Approach for Opinion Polarity Extraction from Twitter

  • Ammar Mars
  • Sihem Hamem
  • Mohamed Salah Gouider
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10449)


Since the past few years, we have been talking about opinion extraction, also known as opinion mining. It is a computational study of opinions and sentiments expressed in a text format. A lot of web resources contain user’s opinions, e.g. social networks, micro blogging platforms, and Blogs. People frequently make their opinions available in these sources. It is important for a company to study the opinions of these customers in order to improve its services or the quality of these products. In this paper, we are interested in studying the opinions of users about a product and extracting their polarity (positive, negative or neutral), for example studying the opinion of users about the Nokia or Huawei brand. We collected data from Twitter because it is a rich data sources for opinion mining. We propose a new ontological approach able to classify the opinion of user’s expressed in their tweets using Natural Language Processing (NLP) tools. This classification used a supervised Machine Learning Classifier: Support Vector Machine (SVM).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ammar Mars
    • 1
  • Sihem Hamem
    • 2
  • Mohamed Salah Gouider
    • 1
    • 3
  1. 1.ISG, SMART LabUniversité de TunisLe BardoTunisia
  2. 2.ISGUniversité de GabesGabesTunisia
  3. 3.ESSECTUniversité de TunisMontfleuryTunisia

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