Arabic Sentiment Analysis Based Machine Learning for Measuring User Satisfaction with Banking Services' Mobile Applications : Comparative Study

Al-Hagree, Salah; Al-Gaphari, Ghaleb

doi:10.1109/esmarta56775.2022.9935486

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Their results showed that combining the LD and NB algorithms yielded the best accuracy of 71%, while the NB algorithm obtained an accuracy of 61%. In [18][19], they introduced a new method to ASA based on the mobile app comments data in Arabic. For the preprocessing of the data, they used the LD algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the Impact of Utilizing the K-Nearest Neighbor and Levenshtein Distance Algorithms for Arabic Sentiment Analysis on Mobile Applications

Al-Shalabi

Al-Hagree

Al-Gaphari

et al. 2023

jast

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The field of sentiment analysis of Arabic scripts remains a major challenge due to the features, characteristics, and complexity of the Arabic language. Few studies on Arabic Sentiment Analysis (ASA) have been conducted to the best of our knowledge when compared to English or other Latin languages. In addition, for ASA, very few studies have been conducted regarding the comments of the users (customers) of the apps available on the Google Play Store within various mobile application reviews. Most of the current studies have been conducted on datasets collected from Twitter user comments. In this paper, we propose a new approach to the analysis of sentiment in Arabic script based on the comments dataset of users of some mobile applications available on the Google Play Store. The proposed approach involves improving algorithms such as the Levenshtein distance (LD) algorithm for data preprocessing and then combining it with the K-Nearest Neighbor (K-NN) algorithm. Through the proposed approach, the results of the experiment were shown, investigating the impact of utilizing the K-NN and LD algorithms for ASA on mobile applications effectively. The experiments were carried out. The K-NN with LD algorithm has gained a better level of evaluation compared to the K-NN algorithm. K-NN with LD algorithm has achieved the highest accuracy, recall, precision, and F-score, which were 83.11% in accuracy, 66.30% in recall, 85.10% in precision, and 74.53% in f-score evaluation measure when =3.

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Section: Literature Reviewmentioning

confidence: 99%