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A Comparative Study of Feature Selection Methods for Informal Arabic

  • Soukaina MihiEmail author
  • Brahim Ait Ben Ali
  • Ismail El Bazi
  • Sara Arezki
  • Nabil Laachfoubi
Conference paper
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Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)

Abstract

The advent of web 2.0 and new Big Data technologies has created a diversity of data and information that can be used in many fields of application. The case of opinion mining is of increasing interest to researchers because of its impact on policy, marketing, etc. Through this document, we are interested in the study of sentiments more specifically in informal Arabic. We present a new approach of processing and analysis that is improved through feature selection methods. The experiments we have carried out are based on the comparison of 3 feature selection methods combined with several machine learning algorithms applied on a twitter dataset. Our paper reports the enhanced results (Accuracy of 98%) and shows the importance of feature selection for Arabic Sentiment Analysis.

Keywords

Sentiment analysis Informal Arabic NLP Feature selection Classification Polarity Social media 
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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Soukaina Mihi
    • 1
    Email author
  • Brahim Ait Ben Ali
    • 1
  • Ismail El Bazi
    • 1
  • Sara Arezki
    • 1
  • Nabil Laachfoubi
    • 1
  1. 1.Faculty of Science and Technology, IR2M LaboratoryHassan 1st UniversitySettatMorocco

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