Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications
Hadwan, Mohammed and Al-Hagery, Mohammed and Al-Sarem, Mohammed and Saeed, Faisal (2022) Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications. Computers, Materials and Continua, 72 (3). pp. 4675-4689. ISSN 1546-2218
|
Text
CMC Paper.pdf - Accepted Version Available under License Creative Commons Attribution. Download (1MB) |
Abstract
Different types of pandemics that have appeared from time to time have changed many aspects of daily life. Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown. The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store. A huge number of reviews are written daily by users to express their opinions, which include significant information to improve these applications. The manual processing and extracting of information from users’ reviews is an extremely difficult and time-consuming task. Therefore, the use of intelligent methods is necessary to analyse users’ reviews and extract issues that can help in improving these apps. This research aims to support the efforts made by the Saudi government for its citizens and residents by analysing the opinions of people in Saudi Arabia that can be found as reviews onGoogle Play and the app store using sentiment analysis and machine learning methods. To the best of our knowledge, this is the first study to explore users’ opinions about governmental apps in Saudi Arabia. The findings of this analysis will help government officers make the right decisions to improve the quality of the provided services and help application developers improve these applications by fixing potential issues that cannot be identified during application testing phases. A new dataset used for this research includes 8000 user reviews gathered from social media, Google Play and the app store. Different methods are applied to the dataset, and the results show that the k nearest neighbourhood (KNN) method generates the highest accuracy compared to other implemented methods.
| Item Type: | Article | ||||||
|---|---|---|---|---|---|---|---|
| Identification Number: | https://doi.org/10.32604/cmc.2022.027311 | ||||||
| Dates: |
|
||||||
| Uncontrolled Keywords: | Arabic sentiment analysis; software quality; user satisfaction; improving online governmental services; machine learning; intelligent systems; mobile app | ||||||
| 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: | 13 May 2022 15:24 | ||||||
| Last Modified: | 13 May 2022 15:24 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/13221 |
Actions (login required)
 |
View Item |
Tools
Tools