A Sentiment Analysis of the Tourist Reviews of the Attractions in Saudi Arabia Using Deep Learning Models
Received: 15 June 2025 | Revised: 18 September 2025, 9 November 2025, 14 December 2025, and 17 December 2025 | Accepted: 18 December 2025 | Online: 9 February 2026
Corresponding author: Raneem Alharbi
Abstract
This paper aims to enhance the standards of tourism and tourist experiences in Saudi Arabia, thereby contributing to the achievement of Saudi Vision 2030 goals. One of these goals is to stimulate economic growth by increasing tourism-related commerce, as Saudi Arabia is becoming an increasingly popular tourist destination. This study employs sentiment analysis of tourist experiences in places recognized as popular attractions by the Saudi Ministry of Tourism. Arabic language tourist reviews of Boulevard Riyadh City, Al-Ula Old Town, the Al-Balad district in Jeddah, the Heritage Village in Dammam, and the Al-Hada cable car from Google Maps are collected, and the sentiment of the reviews is classified as positive, negative, or neutral. The textual representation techniques employed in the model word embedding methods, such as AraVec, ArBERT, Qarib, and MARBERT. Moreover, various models, including baseline models, Deep Learning (DL) models, and transformer-based models, were implemented to predict sentiment. Various metrics, including accuracy, precision, recall, F1-score, and the Area Under the Curve (AUC), were used to evaluate the models' performance. The results demonstrated that QARiB achieved the best performance.
Keywords:
sentiment analysis, CNN, deep learning, transformer-based classificationDownloads
References
J. Gantz and D. Reinsel, Extracting Value from Chaos. Boston MA, USA: International Data Corporation, 2011.
M. Iansiti, "The Value of Data and its Impact on Competition," SSRN Electronic Journal, 2021. DOI: https://doi.org/10.2139/ssrn.3890387
Q. T. Ain et al., "Sentiment Analysis using Deep Learning Techniques: A Review," International Journal of Advanced Computer Science and Applications, vol. 8, no. 6, 2017. DOI: https://doi.org/10.14569/IJACSA.2017.080657
S. Hlee, H. Lee, C. Koo, and N. Chung, "Will the Relevance of Review Language and Destination Attractions be Helpful? A Data-Driven Approach," Journal of Vacation Marketing, vol. 27, no. 1, pp. 61–81, Jan. 2021. DOI: https://doi.org/10.1177/1356766720950356
F. Salem, Social Media and the Internet of Things: Towards Data-Driven Policymaking in the Arab World: Potential, Limits and Concerns, 7th ed. Dubai, UAE: Mohammed Bin Rashid School of Government, 2017.
O. Oueslati, E. Cambria, M. B. Haj-Hmida, and H. Ounelli, "A Review of Sentiment Analysis Research in Arabic Language," Future Generation Computer Systems, vol. 112, pp. 408–430, Nov. 2020. DOI: https://doi.org/10.1016/j.future.2020.05.034
H. S. Rashid, "The Arabic Language in Social Medias' Era," Utopía y Praxis Latinoamericana, vol. 25, no. 1, pp. 356–363, 2020.
R. Al-Jarf, "Effect of Social Media on Arabic Language Attrition," in Sylvester Confab Book of Readings, Abuja, Nigeria: Cissus World Press Books, 2019, pp. 38–48.
A. Farghaly and K. Shaalan, "Arabic Natural Language Processing: Challenges and Solutions," ACM Transactions on Asian Language Information Processing, vol. 8, no. 4, pp. 1–22, Dec. 2009. DOI: https://doi.org/10.1145/1644879.1644881
I. Al-Huri, "Arabic Language: Historic and Sociolinguistic Characteristics," English Literature and Language Review, vol. 1, no. 4, pp. 28–36, 2016.
N. Habash, "Arabic Natural Language Processing," in Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, Dubai, UAE, 2022, pp. 9–10. DOI: https://doi.org/10.18653/v1/2022.emnlp-tutorials.2
E. Boujou, H. Chataoui, A. E. Mekki, S. Benjelloun, I. Chairi, and I. Berrada, "An Open Access NLP Dataset for Arabic Dialects: Data Collection, Labeling, and Model Construction." arXiv, Feb. 07, 2021.
M. Wankhade, A. C. S. Rao, and C. Kulkarni, "A Survey on Sentiment Analysis Methods, Applications, and Challenges," Artificial Intelligence Review, vol. 55, no. 7, pp. 5731–5780, Oct. 2022. DOI: https://doi.org/10.1007/s10462-022-10144-1
B. A. Alharbi, M. A. Mezher, and A. M. Barakeh, "Tourist Reviews Sentiment Classification using Deep Learning Techniques: A Case Study in Saudi Arabia," International Journal of Advanced Computer Science and Applications, vol. 13, no. 6, pp. 717–726, 2022. DOI: https://doi.org/10.14569/IJACSA.2022.0130685
R. AlQadi, H. Al-Nojaidi, L. Alabdulkareem, M. Alrazgan, N. Alghamdi, and M. M. Kamruzzaman, "How Social Media Influencers Affect Consumers' Restaurant Selection: Statistical and Sentiment Analysis," in 2nd International Conference on Computer and Information Sciences, Sakaka, Saudi Arabia, Oct. 2020, pp. 1–6. DOI: https://doi.org/10.1109/ICCIS49240.2020.9257636
W. A. Alasmari, H. A. Abdelhafez, and H. A. Abdelhafez, "Twitter Sentiment Analysis for Reviewing Tourist Destinations in Saudi Arabia using Apache Spark and Machine Learning Algorithms," Journal of Computer Science, vol. 18, no. 3, pp. 215–226, Mar. 2022. DOI: https://doi.org/10.3844/jcssp.2022.215.226
B. Al Sari et al., "Sentiment Analysis for Cruises in Saudi Arabia on Social Media Platforms Using Machine Learning Algorithms," Journal of Big Data, vol. 9, no. 1, Dec. 2022, Art. no. 21. DOI: https://doi.org/10.1186/s40537-022-00568-5
D. Elangovan and V. Subedha, "Adaptive Particle Grey Wolf Optimizer with Deep Learning-Based Sentiment Analysis on Online Product Reviews," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10989–10993, Jun. 2023. DOI: https://doi.org/10.48084/etasr.5787
S. Alosaimi, M. Alharthi, K. Alghamdi, T. Alsubait, and T. Alqurashi, "Sentiment Analysis of Arabic Reviews for Saudi Hotels Using Unsupervised Machine Learning," Journal of Computer Science, vol. 16, no. 9, pp. 1258–1267, Sept. 2020. DOI: https://doi.org/10.3844/jcssp.2020.1258.1267
Banan, "Dataset-Tourist-Places-Reviews." Github, June 2022, [Online]. Available: https://github.com/Banan6/Dataset-Tourist-places-reviews.
P. Jonaitis, "Instant Data Scraper." Web Robots, Vilnius, Lithuania, Jan. 2025, [Online]. Available: https://chromewebstore.google.com/detail/instant-data-scraper/ofaokhiedipichpaobibbnahnkdoiiah.
Raneem, "Saudi Tourism Reviews Sentiment Dataset." Github, Nov. 2025, [Online]. Available: https://github.com/Raneem224/Tourist_Reviews_in-Saudi_Arabia.
N. Al-Twairesh, H. Al-Khalifa, A. Al-Salman, and Y. Al-Ohali, "AraSenTi-Tweet: A Corpus for Arabic Sentiment Analysis of Saudi Tweets," Procedia Computer Science, vol. 117, pp. 63–72, 2017. DOI: https://doi.org/10.1016/j.procs.2017.10.094
G. Salton and C. Buckley, "Term-weighting Approaches in Automatic Text Retrieval," Information Processing & Management, vol. 24, no. 5, pp. 513–523, Jan. 1988. DOI: https://doi.org/10.1016/0306-4573(88)90021-0
A. B. Soliman, K. Eissa, and S. R. El-Beltagy, "AraVec: A set of Arabic Word Embedding Models for use in Arabic NLP," Procedia Computer Science, vol. 117, pp. 256–265, 2017. DOI: https://doi.org/10.1016/j.procs.2017.10.117
M. Abdul-Mageed, A. Elmadany, and E. M. B. Nagoudi, "ARBERT and MARBERT: Deep Bidirectional Transformers for Arabic." arXiv, June 07, 2021. DOI: https://doi.org/10.18653/v1/2021.acl-long.551
A. Abdelali, S. Hassan, H. Mubarak, K. Darwish, and Y. Samih, "Pre-Training BERT on Arabic Tweets: Practical Considerations." arXiv, Feb. 21, 2021.
C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, vol. 20, no. 3, pp. 273–297, Sept. 1995. DOI: https://doi.org/10.1023/A:1022627411411
D. W. Hosmer, S. Lemeshow, and R. X. Sturdivant, Applied Logistic Regression, 1st ed. Hoboken, NJ, USA: Wiley, 2013. DOI: https://doi.org/10.1002/9781118548387
L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001. DOI: https://doi.org/10.1023/A:1010933404324
H. Blockeel, L. Devos, B. Frénay, G. Nanfack, and S. Nijssen, "Decision Trees: From Efficient Prediction to Responsible AI," Frontiers in Artificial Intelligence, vol. 6, July 2023, Art. no. 1124553. DOI: https://doi.org/10.3389/frai.2023.1124553
M. Schuster and K. K. Paliwal, "Bidirectional Recurrent Neural Networks," IEEE Transactions on Signal Processing, vol. 45, no. 11, pp. 2673–2681, Nov. 1997. DOI: https://doi.org/10.1109/78.650093
Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015. DOI: https://doi.org/10.1038/nature14539
K. Cho et al., "Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 1724–1734. DOI: https://doi.org/10.3115/v1/D14-1179
S. Tzimiris, S. Nikiforos, M. N. Nikiforos, D. Mouratidis, and K. L. Kermanidis, "A Comparative Evaluation of Transformer-Based Language Models for Topic-Based Sentiment Analysis," Electronics, vol. 14, no. 15, Jul. 2025, Art. no. 2957. DOI: https://doi.org/10.3390/electronics14152957
Downloads
How to Cite
License
Copyright (c) 2025 Raneem Alharbi, Areej Alshutayri, Shahd Alahdal
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
