Sentiment Analysis Model to Predict People’s Opinion of the Trimester System in Saudi Arabia

Sentiment Analysis Model to Predict People’s Opinion of the Trimester System in Saudi Arabia

  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-2
Year of Publication : 2023
Author : Mashael M. Alsulami
DOI : 10.14445/22315381/IJETT-V71I2P246

How to Cite?

Mashael M. Alsulami, "Sentiment Analysis Model to Predict People’s Opinion of the Trimester System in Saudi Arabia ," International Journal of Engineering Trends and Technology, vol. 71, no. 2, pp. 450-456, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I2P246

Abstract
The trimester system is a new academic system in the education sector in Saudi Arabia. At the beginning of 2021, the Ministry of Education announced the introduction of the trimester system in general education, aiming to overcome the gap between the actual number of study hours in Saudi Arabia and those in international educational systems. This paper, using the sentiment analysis of Twitter data, investigated people’s opinions about the trimester system. We extracted and conducted a multi-class classification model using several machine learning classifiers to classify each tweet in terms of its sentiment polarity appropriately. Results showed that both Linear Regression and Random Forest classifiers achieved better performance with multi-class models than other classifiers. The analysis results showed a neutral emotional state of most Saudi users regarding the trimester system. This indicates a need to explain the policies and changes regarding this system to people, so they understand this system better. The results of this research could help decision makers to understand the emotional aspects of the trimester system in the Saudi community.

Keywords
Sentiment analysis, Opinion mining, Machine learning, and Governmental services.

References
[1] Mrs. Shoayee Alotaibi et al., “Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning,” Applied Science, vol. 10, no. 4, 2020. Crossref, https://doi.org/10.3390/app10041398
[2] Mashael M. Alsulami, and Rashid Mehmood, “Sentiment Analysis Model for Arabic Tweets to Detect Users’ Opinions About Government Services in Saudi Arabia: Ministry of Education as a Case Study,” Alyamamah Information Communication Technology Conference, 2018.
[3] Sumayh S. Aljameel et al., “A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent Covid-19 Outbreaks in Saudi Arabia,” International Journal of Environmental Research Public Health, vol. 18, no. 1, pp. 1–12, 2021. Crossref, https://doi.org/10.3390/ijerph18010218
[4] Mohammed Alhajji et al., “Sentiment Analysis of Tweets in Saudi Arabia Regarding Governmental Preventive Measures to Contain COVID-19,” Preprints, p. 16, 2020. Crossref, https://doi.org/10.20944/preprints202004.0031.v1
[5] Sufyan Areed et al., “Aspect-Based Sentiment Analysis for Arabic Government Reviews,” Studies in Computational Intelligence vol. 874, pp. 143–162, 2020. Crossref, https://doi.org/10.1007/978-3-030-34614-0_8
[6] Priyanka, and Kirti Walia, "Cuckoo Search Optimized Improved Opinion Mining and Classification," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 44-53, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P206
[7] Jaewoong Choi et al., “Social Media Analytics and Business Intelligence Research: A Systematic Review,” Information Processing & Management, vol. 57, no. 6, 2020. Crossref, https://doi.org/10.1016/j.ipm.2020.102279
[8] J. Ram, and C. Zhang, “Examining the Role of Social Media Analytics in Providing Competitive Intelligence: the Impacts and Limitations,” Journal of Global Information Management, vol. 29, no. 6, p. 18, 2021. Crossref, https://doi.org/10.4018/JGIM.20211101.oa15
[9] Yung-Chun Chang, Chih-Hao Ku, and Chun-Hung Chen, “Social Media Analytics: Extracting and Visualizing Hilton Hotel Ratings and Reviews from Tripadvisor,” International Journal of Information Management, vol. 48, pp. 263–272, 2019. Crossref, https://doi.org/10.1016/j.ijinfomgt.2017.11.001
[10] Arno Scharl et al., Tourism Intelligence and Visual Media Analytics for Destination Management Organizations, Tourism on the Verge, pp. 165-178, 2017. Crossref, https://doi.org/10.1007/978-3-319-44263-1_10
[11] Asif Ansari, and Sreenarayanan NM, "Analysis of Text Classification of Dataset Using NB-Classifier," SSRG International Journal of Computer Science and Engineering, vol. 7, no. 6, pp. 24-28, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I6P107
[12] Biraj Dahal, Sathish A. P. Kumar, and Zhenlong Li, “Topic Modeling and Sentiment Analysis of Global Climate Change Tweets,” Social Network Analysis and Mining, vol. 9, no. 1, 2019. Crossref, https://doi.org/10.1007/s13278-019-0568-8
[13] Samira Zad, “A Survey on Concept-Level Sentiment Analysis Techniques of Textual Data,” IEEE World AI Iot Congress AI IOT 2021, pp. 285–291, 2021. Crossref, https://doi.org/10.1109/AIIoT52608.2021.9454169
[14] Puspita Kencana Sari et al., “Measuring E-Commerce Service Quality from Online Customer Review Using Sentiment Analysis,” Journal of Physics: Conference Series, vol. 971, no. 1, 2018. Crossref, https://doi.org/10.1088/1742-6596/971/1/012053
[15] Rafulla Mohapatra et al., "Sentiment Classification of Movie Review and Twitter Data Using Machine Learning," International Journal of Computer and Organization Trends, vol. 9, no. 3, pp. 1-8, 2019. Crossref, https://doi.org/10.14445/22492593/IJCOT-V9I3P301
[16] Malak Aljabri et al., “Sentiment Analysis of Arabic Tweets Regarding Distance Learning in Saudi Arabia During the Covid-19 Pandemic,” Sensors, vol. 21, no. 16, 2021. Crossref, https://doi.org/10.3390/s21165431
[17] Asma Althagafi et al., “Arabic Tweets Sentiment Analysis About Online Learning During COVID-19 in Saudi Arabia,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 3, pp. 620–625, 2021.
[18] Madhuri V. Joseph, "Sentiment Analysis of Amazon Review Using Improvised Conditional Based Convolutional Neural Network and Word Embedding," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 194-209, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P220
[19] S. S. — Twitter Reaches Half a Billion Accounts — M. Than 140 Millions in the U. U.S, Semiocast Semiocast — Twitter Reaches Half a Billion Accounts — More Than 140 Millions in the U.S, 2012. [Online]. Available: http://semiocast.com/en/publications/2012_07_30_twitter_reaches_half_a_billion_accounts_140m_in_the_us
[20] J. Roesslein, Tweepy: Twitter for Python!, [Online]. Available: URL https//github.com/tweepy/tweepy, 2020.
[21] T. 2.1.20, Twint 2.1.20, Twint 2.1.20 Documentation, 2020. .
[22] Motaz Saad, Arabic Text Classification: Text Preprocessing, Term Weighting, and Morphological Analysis, Lap Lambert Academic Publishin, p. 172, 2011.
[23] Himanshu Thakur, and Aman Kumar Sharma, "Supervised Machine Learning Classifiers: Computation of Best Result of Classification Accuracy," International Journal of Computer Trends and Technology, vol. 68, no. 10, pp. 1-8, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I10P101
[24] Satwik Mishra, “Handling Imbalanced Data : SMOTE vs Random Undersampling,” International Research Journal of Engineering and Technology, vol. 4, no. 8, pp. 317–320, 2017.
[25] Ibrahim Abu Farha, and Walid Magdy, “Mazajak: An Online Arabic Sentiment Analyser,” Proceedings of the Fourth Arabic Natural Language Processing Workshop, pp. 192-198, 2019. Crossref, http://dx.doi.org/10.18653/v1/W19-4621
[26] J. R. Landis, and G. G. Koch, “The Measurement of Observer Agreement for Categorical Data,” Biometrics, vol. 33, no. 1, p. 159, 1977.
[27] Nesime Tatbul et al, “Precision and Recall for Time Series,” Conference on Neural Information Processing Systems, vol. 2018, pp. 1920– 1930, 2018.
[28] K. Kavitha, and Suneetha Chittineni, "Efficient Sentimental Analysis Using Hybrid Deep Transfer Learning Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 155-165, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P216
[29] Kareem Mohamed, and Ümmü Altan Bayraktar, "Analyzing the Role of Sentiment Analysis in Public Relations: Brand Monitoring and Crisis Management," SSRG International Journal of Humanities and Social Science, vol. 9, no. 3, pp. 116-126, 2022. Crossref, https://doi.org/10.14445/23942703/IJHSS-V9I3P116
[30] Emitza Guzman, R. Alkadhi, and N. Seyff, “An Exploratory Study of Twitter Messages About Software Applications,” Requirements Engineering, vol. 22, no. 3, pp. 387–412, 2017. Crossref, https://doi.org/10.1007/s00766-017-0274-x
[31] Ghazaleh Beigi et al., “An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief,” Studies in Computational Intelligence, vol. 639, pp. 313–340, 2016. Crossref, https://doi.org/10.1007/978-3-319-30319-2_13
[32] H. Mubarak, and K. Darwish, “Using Twitter to Collect a Multi-Diacorpus of Arabic,” Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP), pp. 1-7, 2014. Crossref, https://aclanthology.org/W14-3601
[33] Tawfiq Hasanin, and Taghi Khoshgoftaar, “The Effects of Random Undersampling with Simulated Class Imbalance for Big Data,” IEEE International Conference on Information Reuse and Integration (IRI), 2018. Crossref, https://doi.org/10.1109/IRI.2018.00018
[34] Nan Hu et al., “Ratings Lead You to the Product , Reviews Help You Clinch It ? The Mediating Role of Online Review Sentiments on Product Sales,” Decision Support Systems vol. 57, pp. 42–53, 2014. Crossref, https://doi.org/10.1016/j.dss.2013.07.009