Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Authors

  • Hawraa Fadhil Khelil Technical College of Management - Baghdad, Middle Technical University, Baghdad, Iraq.
  • Mohammed Fadhil Ibrahim School of Computer Science, Universiti Sains Malaysia (USM), Penang, Malaysia
  • Hafsa Ataallah Hussein Technical College of Management - Baghdad, Middle Technical University, Baghdad, Iraq.
  • Raed Kamil Naser Universiti Sains Malaysia (USM), Penang, Malaysia

DOI:

https://doi.org/10.51173/jt.v6i2.2313

Keywords:

NLP, KNN, NB, LR, Snowball Stemmer, Khoja Stemmer, Tashaphyne Stemmer

Abstract

Customer opinion and reviews play a vital role in marketing expansion. Big companies all over the world assign a lot of their efforts to analyzing customers’ feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. Arabic customer analysis and classification also began to gain researchers’ attention due to the wide range of Arabic language speakers. Working with Arabic Language is a very challenging task because of the orthographic nature of Arabic. Also, customers often write their reviews in their dialectical style, which often diverts from standard Arabic. This study presents a method to classify Arabic customer reviews using four classifiers (K-nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (RL), and Naïve Bayes (NB)). The classification is implemented with three stemming techniques (Snowball, Khoja, and Tashaphyne). The HARD dataset is adopted to perform the experiments. The results stated that the stemming methods can enhance classification performance despite the complexity of Arabic scripts and dialects. This work sheds light on utilizing and investigating more machine learning (ML) techniques and evaluating the results. 

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Author Biographies

Hawraa Fadhil Khelil, Technical College of Management - Baghdad, Middle Technical University, Baghdad, Iraq.

      

Mohammed Fadhil Ibrahim, School of Computer Science, Universiti Sains Malaysia (USM), Penang, Malaysia

       

Hafsa Ataallah Hussein, Technical College of Management - Baghdad, Middle Technical University, Baghdad, Iraq.

     

Raed Kamil Naser, Universiti Sains Malaysia (USM), Penang, Malaysia

      

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Published

2024-02-15

How to Cite

Hawraa Fadhil Khelil, Mohammed Fadhil Ibrahim, Hafsa Ataallah Hussein, & Raed Kamil Naser. (2024). Evaluation of Different Stemming Techniques on Arabic Customer Reviews. Journal of Techniques, 6(2), 1–8. https://doi.org/10.51173/jt.v6i2.2313

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Information Technology

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