| Autor: |
Khelil, Hawraa Fadhil, Ibrahim, Mohammed Fadhil, Hussein, Hafsa Ataallah, Naser, Raed Kamil |
| Předmět: |
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| Zdroj: |
Journal of Techniques; Jun2024, Vol. 6 Issue 2, p1-8, 8p |
| Abstrakt: |
Customer opinions and reviews play a vital role in marketing expansion. Big companies worldwide 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 dialectically, diverting 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 phase uses three stemming techniques (Snowball, Khoja, and Tashaphyne). The HARD dataset is adopted to perform the experiments. The results stated that the stemming methods could enhance classification performance despite the complexity of Arabic scripts and dialects, where the best accuracy of the results was 91% when using SVM and LR with Snowball Stemmer. This work sheds light on utilizing and investigating more machine learning (ML) techniques and evaluating the results. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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