An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique

Hadwan, Mohammed and Al-Sarem, Mohammed and Saeed, Faisal and Al-Hagery, Mohammed (2022) An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique. Applied Sciences, 12 (11). p. 5547. ISSN 2076-3417

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Abstract

Analyzing the sentiment of Arabic texts is still a big research challenge due to the special characteristics and complexity of the Arabic language. Few studies have been conducted on Arabic sentiment analysis (ASA) compared to English or other Latin languages. In addition, most of the existing studies on ASA analyzed datasets collected from Twitter. However, little attention was given to the huge amounts of reviews for governmental or commercial mobile applications on Google Play or the App Store. For instance, the government of Saudi Arabia developed several mobile applications in healthcare, education, and other sectors as a response to the COVID-19 pandemic. To address this gap, this paper aims to analyze the users’ opinions of six applications in the healthcare sector. An improved sentiment classification approach was proposed for measuring user satisfaction toward governmental services’ mobile apps using machine learning models with different preprocessing methods. The Arb-AppsReview dataset was collected from the reviews of these six mobile applications available on Google Play and the App Store, which includes 51k reviews. Then, several feature engineering approaches were applied, which include Bing Liu lexicon, AFINN, and MPQA Subjectivity Lexicon, bag of words (BoW), term frequency-inverse document frequency (TF-IDF), and the Google pre-trained Word2Vec. Additionally, the SMOTE technique was applied as a balancing technique on this dataset. Then, five ML models were applied to classify the sentiment opinions. The experimental results showed that the highest accuracy score (94.38%) was obtained by applying a support vector machine (SVM) using the SMOTE technique with all concatenated features.

Item Type: Article
Identification Number: https://doi.org/10.3390/app12115547
Dates:
DateEvent
27 May 2022Accepted
30 May 2022Published Online
Uncontrolled Keywords: sentiment analysis; machine learning; classification; ensemble learning; feature selection
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Faisal Saeed
Date Deposited: 01 Jul 2022 15:15
Last Modified: 01 Jul 2022 15:15
URI: http://www.open-access.bcu.ac.uk/id/eprint/13368

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