2
How to cite this paper
APA: Shenify, M. (2025). Sentiment analysis of social media discourse on public perception of online courier services in Saudi Arabia using machine learning. International Journal of Data and Network Science, 9(1), 217-226.
Chicago/Turabian: Shenify, M. 2025. "Sentiment analysis of social media discourse on public perception of online courier services in Saudi Arabia using machine learning." International Journal of Data and Network Science 9, no. 1 (2025): 217-226.
AMA: Shenify, M. Sentiment analysis of social media discourse on public perception of online courier services in Saudi Arabia using machine learning. International Journal of Data and Network Science. 2025;9(1):217-226.
References
Adak, A., Pradhan, B., Shukla, N. (2022). Sentiment analysis of customer reviews of food delivery services using deep learning and explainable artificial intelligence: Systematic review. Foods, 11(10):1500. doi: 10.3390/foods11101500.
Al Sari, B., Alkhaldi, R., Alsaffar, D., Alkhaldi, T., Almaymuni, H., Alnaim, N., ... & Olatunji, S. O. (2022). Sentiment analysis for cruises in Saudi Arabia on social media platforms using machine learning algorithms. Journal of big Data, 9(1), 21. https://doi.org/10.1186/s40537-022-00568-5.
Dang, N. C, Moreno-García, M. N., & De la Prieta, F. (2020). Sentiment analysis based on deep learning: A comparative study. Electronics, 9(3), 483. https://doi.org/10.3390/electronics9030483.
Elhag, M., Abo, M., Idris, N., Mahmud, R., & Qazi, A. (2021). A multi-criteria approach for arabic dialect sentiment analysis for online reviews : exploiting optimal machine learning algorithm selection.Sustainability, 13(18), 1-20.
Gulo, E.S., Gulo, Y.R., & Marbun, S.F. (2022). Comparison of the effectiveness of decision tree, naïve bayes, k-nearest neighbor and support vector machine algorithms in carrying out classification, JUTIKOMP, 5(2), 54-9.
Hakami, N.A. (2023). Identification of customers satisfaction with popular online shopping apps in Saudi Arabia using sentiment analysis and topic modeling. In Proceedings of the 2023 7th International Conference on E-Commerce, E-Business and E-Government (ICEEG '23). Association for Computing Machinery, New York, NY, USA, 1–11. https://doi.org/10.1145/3599609.3599610.
Mohammadi, M. Rashid, T. A., Karim, S. H. T., Aldalwie, A. H. M., Tho, Q. T., Bidaki, M., Rahmani, A. M., & Hosseinzadeh, M. (2021). A comprehensive survey and taxonomy of the SVM-based intrusion detection systems, Journal of Network and Computer Applications, 178, 102983.
Mordor Intelligent Report, (2024). Saudi Arabia courier, express, and parcel (CEP) market trends. Available online: https://www.mordorintelligence.com/industry-reports/saudi-arabia-courier-express-and-parcel-cep-market. Last access: 1 May 2024.
Nisbet, R., Miner, G., and Yale, K, (2018). Handbook of Statistical Analysis and Data Mining Applications, Second Edition, Academic Press,
Prabhakar, E. Santhosh, M., Krishnan, A. H., Kumar, T. and Sudhakar, R. (2019). Sentiment analysis of US airline twitter data using new adaboost approach. International Journal of Engineering Resource Technology, 7(01), 1–3, 2019.
Salem, M. A., & Md Nor, K. (2020). The effect of COVID-19 on consumer behaviour in Saudi Arabia: Switching from brick and mortar stores to E-commerce. International Journal of Scientific & Technology Research, 9(07).
Semary, N. A., Ahmed, W., Amin K., Pławiak P., & Hammad M. (2024). Enhancing machine learning-based sentiment analysis through feature extraction techniques. PLoS One, 19(2):e0294968. doi: 10.1371/journal.pone.0294968.
Sghaier, M. A., & Zrigui, M. (2016). Sentiment analysis for Arabic e-commerce websites. In the proceedings of 2016 International Conference on Engineering & MIS (ICEMIS), Agadir, Morocco, 2016, pp. 1-7, doi: 10.1109/ICEMIS.2016.7745323.
Sobaih, A. E. E., & AlSaif, A. (2023). Effects of parcel delivery service on customer satisfaction in the Saudi Arabian logistics industry: Does the national culture make a difference? Logistics, 7(4), 94. https://doi.org/10.3390/logistics7040094
Tuan, T. A., Long, H. V., Son, L. H., Kumar, R., Priyadarshini, I., & Son, N. T. K. (2020). Performance evaluation of Botnet DDoS attack detection using machine learning. Evolutionary Intelligence, 13(2), 283-294.
Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Neural Information Processing Systems. https://arxiv.org/pdf/1706.03762.
Wang, N. Li, G., & Wang, Z. (2023). Fast SVM classifier for large-scale classification problems, Information Sciences, 642.
Wasiq, M., Johri, A., Singh, P. (2022). Factors affecting adoption and use of M-commerce services among the customers in Saudi Arabia, Heliyon, 8(12), e12532. https://doi.org/10.1016/j.heliyon.2022.e12532.
Zhang, S., Zhong, H., Wei, C., Zhang, D. (2022) Research on logistics service assessment for smart city: A users’ review sentiment analysis approach. Electronics, 11(23), 4018. https://doi.org/10.3390/electronics11234018.
Zygiaris, S., Hameed, Z., Alsubaie, M. A., & Ur Rehman, S. (2022). Service quality and customer satisfaction in the post pandemic world: A study of Saudi auto care industry, Frontiers in Psychology, 2022, Sec. Organizational Psychology, 13 - 2022 https://doi.org/10.3389/fpsyg.2022.842141.
Al Sari, B., Alkhaldi, R., Alsaffar, D., Alkhaldi, T., Almaymuni, H., Alnaim, N., ... & Olatunji, S. O. (2022). Sentiment analysis for cruises in Saudi Arabia on social media platforms using machine learning algorithms. Journal of big Data, 9(1), 21. https://doi.org/10.1186/s40537-022-00568-5.
Dang, N. C, Moreno-García, M. N., & De la Prieta, F. (2020). Sentiment analysis based on deep learning: A comparative study. Electronics, 9(3), 483. https://doi.org/10.3390/electronics9030483.
Elhag, M., Abo, M., Idris, N., Mahmud, R., & Qazi, A. (2021). A multi-criteria approach for arabic dialect sentiment analysis for online reviews : exploiting optimal machine learning algorithm selection.Sustainability, 13(18), 1-20.
Gulo, E.S., Gulo, Y.R., & Marbun, S.F. (2022). Comparison of the effectiveness of decision tree, naïve bayes, k-nearest neighbor and support vector machine algorithms in carrying out classification, JUTIKOMP, 5(2), 54-9.
Hakami, N.A. (2023). Identification of customers satisfaction with popular online shopping apps in Saudi Arabia using sentiment analysis and topic modeling. In Proceedings of the 2023 7th International Conference on E-Commerce, E-Business and E-Government (ICEEG '23). Association for Computing Machinery, New York, NY, USA, 1–11. https://doi.org/10.1145/3599609.3599610.
Mohammadi, M. Rashid, T. A., Karim, S. H. T., Aldalwie, A. H. M., Tho, Q. T., Bidaki, M., Rahmani, A. M., & Hosseinzadeh, M. (2021). A comprehensive survey and taxonomy of the SVM-based intrusion detection systems, Journal of Network and Computer Applications, 178, 102983.
Mordor Intelligent Report, (2024). Saudi Arabia courier, express, and parcel (CEP) market trends. Available online: https://www.mordorintelligence.com/industry-reports/saudi-arabia-courier-express-and-parcel-cep-market. Last access: 1 May 2024.
Nisbet, R., Miner, G., and Yale, K, (2018). Handbook of Statistical Analysis and Data Mining Applications, Second Edition, Academic Press,
Prabhakar, E. Santhosh, M., Krishnan, A. H., Kumar, T. and Sudhakar, R. (2019). Sentiment analysis of US airline twitter data using new adaboost approach. International Journal of Engineering Resource Technology, 7(01), 1–3, 2019.
Salem, M. A., & Md Nor, K. (2020). The effect of COVID-19 on consumer behaviour in Saudi Arabia: Switching from brick and mortar stores to E-commerce. International Journal of Scientific & Technology Research, 9(07).
Semary, N. A., Ahmed, W., Amin K., Pławiak P., & Hammad M. (2024). Enhancing machine learning-based sentiment analysis through feature extraction techniques. PLoS One, 19(2):e0294968. doi: 10.1371/journal.pone.0294968.
Sghaier, M. A., & Zrigui, M. (2016). Sentiment analysis for Arabic e-commerce websites. In the proceedings of 2016 International Conference on Engineering & MIS (ICEMIS), Agadir, Morocco, 2016, pp. 1-7, doi: 10.1109/ICEMIS.2016.7745323.
Sobaih, A. E. E., & AlSaif, A. (2023). Effects of parcel delivery service on customer satisfaction in the Saudi Arabian logistics industry: Does the national culture make a difference? Logistics, 7(4), 94. https://doi.org/10.3390/logistics7040094
Tuan, T. A., Long, H. V., Son, L. H., Kumar, R., Priyadarshini, I., & Son, N. T. K. (2020). Performance evaluation of Botnet DDoS attack detection using machine learning. Evolutionary Intelligence, 13(2), 283-294.
Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Neural Information Processing Systems. https://arxiv.org/pdf/1706.03762.
Wang, N. Li, G., & Wang, Z. (2023). Fast SVM classifier for large-scale classification problems, Information Sciences, 642.
Wasiq, M., Johri, A., Singh, P. (2022). Factors affecting adoption and use of M-commerce services among the customers in Saudi Arabia, Heliyon, 8(12), e12532. https://doi.org/10.1016/j.heliyon.2022.e12532.
Zhang, S., Zhong, H., Wei, C., Zhang, D. (2022) Research on logistics service assessment for smart city: A users’ review sentiment analysis approach. Electronics, 11(23), 4018. https://doi.org/10.3390/electronics11234018.
Zygiaris, S., Hameed, Z., Alsubaie, M. A., & Ur Rehman, S. (2022). Service quality and customer satisfaction in the post pandemic world: A study of Saudi auto care industry, Frontiers in Psychology, 2022, Sec. Organizational Psychology, 13 - 2022 https://doi.org/10.3389/fpsyg.2022.842141.