Arabic Tweets Sentimental Analysis Using Machine Learning

  • Khaled Mohammad Alomari
  • Hatem M. ElSherif
  • Khaled Shaalan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10350)

Abstract

The continuous rapid growth of electronic Arabic contents in social media channels and in Twitter particularly poses an opportunity for opinion mining research. Nevertheless, it is hindered by either the lack of sentimental analysis resources or Arabic language text analysis challenges. This study introduces an Arabic Jordanian twitter corpus where Tweets are annotated as either positive or negative. It investigates different supervised machine learning sentiment analysis approaches when applied to Arabic user’s social media of general subjects that are found in either Modern Standard Arabic (MSA) or Jordanian dialect. Experiments are conducted to evaluate the use of different weight schemes, stemming and N-grams terms techniques and scenarios. The experimental results provide the best scenario for each classifier and indicate that SVM classifier using term frequency–inverse document frequency (TF-IDF) weighting scheme with stemming through Bigrams feature outperforms the Naïve Bayesian classifier best scenario performance results. Furthermore, this study results outperformed other results from comparable related work.

Keywords

Sentiment analysis Machine learning Arabic natural language processing 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Arts and SciencesAbu Dhabi UniversityAbu DhabiUAE
  2. 2.Faculty of Engineering and ITThe British University in DubaiDubaiUAE

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