Applied Intelligence

, Volume 49, Issue 5, pp 1688–1707 | Cite as

Improved whale optimization algorithm for feature selection in Arabic sentiment analysis

  • Mohammad Tubishat
  • Mohammad A. M. Abushariah
  • Norisma IdrisEmail author
  • Ibrahim Aljarah


To help individuals or companies make a systematic and more accurate decisions, sentiment analysis (SA) is used to evaluate the polarity of reviews. In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. However, WOA suffers from the same problem faced by many other optimization algorithms and tend to fall in local optima. To overcome these problems, two improvements for WOA algorithm are proposed in this paper. The first improvement includes using Elite Opposition-Based Learning (EOBL) at initialization phase of WOA. The second improvement involves the incorporation of evolutionary operators from Differential Evolution algorithm at the end of each WOA iteration including mutation, crossover, and selection operators. In addition, we also used Information Gain (IG) as a filter features selection technique with WOA using Support Vector Machine (SVM) classifier to reduce the search space explored by WOA. To verify our proposed approach, four Arabic benchmark datasets for sentiment analysis are used since there are only a few studies in sentiment analysis conducted for Arabic language as compared to English. The proposed algorithm is compared with six well-known optimization algorithms and two deep learning algorithms. The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.


Arabic sentiment analysis Support vector machine Information gain Whale optimization algorithm 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Mohammad Tubishat
    • 1
  • Mohammad A. M. Abushariah
    • 2
  • Norisma Idris
    • 1
    Email author
  • Ibrahim Aljarah
    • 3
  1. 1.Department of Artificial Intelligence, Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  2. 2.Computer Information Systems Department, King Abdullah II School of Information TechnologyThe University of JordanAmmanJordan
  3. 3.Business Information Technology Department, King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan

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