Utilizing Deep Learning for Aspect-Based Sentiment Analysis in Restaurant Reviews

Document Type : Research Paper

Authors

1 Associate Prof., Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran.

2 Assistant prof., Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.

3 MSc, Department of Social Science and Economics, Alzahra Univerity, Tehran, Iran.

4 MSc student, Department of Social Science and Economics, Alzahra Univerity, Tehran, Iran.

Abstract

Consumers rely on social media opinions to make product choices and purchases. With the popularity of web-based platforms like Tripadvisor, consumers express their opinions and feelings about food quality, service, and other aspects affecting restaurants through comments. Hence, analyzing these comments can be valuable for others to choose a restaurant or to improve and develop their products and brands. Sentiment analysis utilizes text mining methods to extract, identify, and study emotions and subjective perceptions. Since consumers can use comments to choose a restaurant, this study seeks to provide sentiment analysis of their reviews on the Tripadvisor website about Iranian restaurants. This study is applied in nature, aiming to analyze and manually label 4000 comments from the Tripadvisor website regarding restaurants in ten tourist cities across Iran. It uses a standard extended long short-term memory algorithm for sentiment analysis, a deep learning neural network, and Python text mining packages for modeling. The results indicate that the F-Measure for all aspects exceeds 80%, indicating sufficient efficiency and accuracy of the aspect-based sentiment analysis model for restaurant reviews. The most significant features for customers of Iranian restaurants are the food and the atmosphere. This study represents one of the initial efforts to analyze comments posted on the Tripadvisor website concerning Iranian restaurants. Business owners in the tourism industry, especially restaurant owners, can use the proposed model to automatically and quickly analyze customer feedback, improve performance, and gain a competitive edge. The proposed model can also assist users of online platforms in analyzing the opinions of others, enabling them to make informed decisions more efficiently.

Keywords

Main Subjects

References

Abbasi, F., Khadivar, A., & Yazdinejad, M. (2019). A Grouping Hotel Recommender System Based on Deep Learning and Sentiment Analysis. Journal of Information Technology Management, 11(2), 59-78. https://doi.org/10.22059/jitm.2019.289271.2402
Agarwal, B., Nayak, R., Mittal, N., & Patnaik, S. (2020). Deep Learning-Based Approaches for Sentiment Analysis. Springer Nature Singapore. https://books.google.bg/books?id=tSTMDwAAQBAJ
Al-Smadi, M., Qawasmeh, O., Al-Ayyoub, M., Jararweh, Y., & Gupta, B. (2018). Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. Journal of Computational Science, 27, 386-393. https://doi.org/https://doi.org/10.1016/j.jocs.2017.11.006
Al-Smadi, M., Talafha, B., Al-Ayyoub, M., & Jararweh, Y. (2019). Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. International Journal of Machine Learning and Cybernetics, 10(8), 2163-2175. https://doi.org/10.1007/s13042-018-0799-4
Alaei, A. R., Becken, S., & Stantic, B. (2017). Sentiment Analysis in Tourism: Capitalizing on Big Data. Journal of Travel Research, 58(2), 175-191. https://doi.org/10.1177/0047287517747753
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0 Step-by-step data mining guide.
Dami, S., & Mohammadi, S. (2017). Opinion mining in tourism with unsupervised learning. First National Conference on Computer Engineering and Information Technology, Sepidan.
Dang, N. C., Moreno-García, M. N., & De la Prieta, F. (2020). Sentiment Analysis Based on Deep Learning: A Comparative Study. Electronics, 9(3).
Dhurve, R., & Seth, M. (2015). Weighted Sentiment Analysis Using Artificial Bee Colony Algorithm.
Geetha, M., Singha, P., & Sinha, S. (2017). Relationship between customer sentiment and online customer ratings for hotels - An empirical analysis. Tourism Management, 61, 43-54. https://doi.org/10.1016/j.tourman.2016.12.022
Guha, S., Joshi, A., & Varma, V. (2015). SIEL: Aspect Based Sentiment Analysis in Reviews. https://doi.org/10.18653/v1/S15-2129
Haghighi, M., Dorosti, A., Rahnama, A., & Hoseinpour, A. (2012). Evaluation of factors affecting customer loyalty in the restaurant industry. African Journal of Business Management, 6. https://doi.org/10.5897/AJBM11.2765
Jia, S. (2018). Behind the ratings: Text mining of restaurant customers’ online reviews. International Journal of Market Research, 60(6), 561-572. https://doi.org/10.1177/1470785317752048
Kamalpour, M., Rezaei Aghdam, A., Xu, S., Khani, E., & Baghi, A. (2017). Uncovering Hotel Guests Preferences through Data Mining Techniques.
Karimi, F., Khadivar, A., & Abbasi, F. (2024). Classification of user Comments on Virtual Reality Technology by Topic Modeling. Business Intelligence Management Studies, 12(47). https://doi.org/10.22054/ims.2023.74147.2342
Kharadi, B., & Patel, K. (2017). Opinion Mining of Restaurant Review by Sentiment Analysis Using SVM.
Khorsand, R., Rafiee, M., & Kayvanfar, V. (2020). Insights into TripAdvisor's online reviews: The case of Tehran's hotels. Tourism Management Perspectives, 34, 100673. https://doi.org/10.1016/j.tmp.2020.100673
Kulesza, T., Amershi, S., Caruana, R., Fisher, D., & Charles, D. (2014). Structured labeling for facilitating concept evolution in machine learning. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, Ontario, Canada. https://doi.org/10.1145/2556288.2557238
Li, J. B., & Yang, L. B. (2017, 4-6 Nov. 2017). A Rule-Based Chinese Sentiment Mining System with Self-Expanding Dictionary - Taking TripAdvisor as an Example. 2017 IEEE 14th International Conference on e-Business Engineering (ICEBE).
Liu, N., & Shen, B. (2020). Aspect-based sentiment analysis with gated alternate neural network. Knowledge-Based Systems, 188, 105010. https://doi.org/10.1016/j.knosys.2019.105010
Ma, Y., Peng, H., & Cambria, E. (2018). Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM.
Mäntylä, M. V., Graziotin, D., & Kuutila, M. (2018). The evolution of sentiment analysis—A review of research topics, venues, and top cited papers. Computer Science Review, 27, 16-32. https://doi.org/10.1016/j.cosrev.2017.10.002
Maw, S. Y., & Khine, M. A. (2019). Aspect based Sentiment Analysis for travel and tourism in Myanmar Language using LSTM. http://onlineresource.ucsy.edu.mm/handle/123456789/1190
Nguyen, H., & Shirai, K. (2018, 1-3 Nov. 2018). A Joint Model of Term Extraction and Polarity Classification for Aspect-based Sentiment Analysis. 2018 10th International Conference on Knowledge and Systems Engineering (KSE).
Noroozi, M., Khadivar, A., & Abbasi, F. (2023). Modeling and predicting mobile phone purchase intention of Twitter users based on sentiment analysis. Modern Researches In Decision Making, 8(1), 91-112.
Nyst, A. (2023). 134 Social Media Statistics You Need To Know For 2023. https://www.searchenginejournal.com/social-media-statistics/480507/
Park, S. B., Jang, J., & Ok, C. M. (2016). Analyzing Twitter to explore perceptions of Asian restaurants. Journal of Hospitality and Tourism Technology, 7(4), 405-422. https://doi.org/10.1108/JHTT-08-2016-0042
Qiang, Y., Li, X., & Zhu, D. (2020). Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach.
Roshanfekr, B., Khadivi, S., & Rahmati, M. (2017). Sentiment analysis using deep learning on Persian texts. 2017 Iranian Conference on Electrical Engineering (ICEE), 1503-1508.
Ruder, S., Ghaffari, P., & Breslin, J. (2016). A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis. https://doi.org/10.18653/v1/D16-1103
Sahar, N. u., Sohail Irshad, M., & Adnan Khan, M. (2019). Bayesian Sentiment Analytics for Emerging Trends in Unstructured Data Streams. EAI Endorsed Transactions on Scalable Information Systems, 6(22), e5. https://doi.org/10.4108/eai.13-7-2018.159355
Schouten, K., & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830. https://doi.org/10.1109/TKDE.2015.2485209
Shafique, U., & Qaiser, H. (2014). A Comparative Study of Data Mining Process Models (KDD, CRISP-DM and SEMMA). International Journal of Innovation and Scientific Research, 12, 2351-8014.
Singh, N. K., Tomar, D. S., & Sangaiah, A. K. (2020). Sentiment analysis: a review and comparative analysis over social media. Journal of Ambient Intelligence and Humanized Computing, 11(1), 97-117. https://doi.org/10.1007/s12652-018-0862-8
Tan, S., Wang, Y., & Cheng, X. (2008). Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples. Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Singapore, Singapore. https://doi.org/10.1145/1390334.1390481
Tyagi, E., & Sharma, D. (2017). Sentiment Analysis of Product Reviews using Support Vector Machine Learning Algorithm. Indian Journal of Science and Technology, 10, 1-9. https://doi.org/10.17485/ijst/2017/v10i35/118965
Vu, H. Q., Li, G., Law, R., & Zhang, Y. (2017). Exploring Tourist Dining Preferences Based on Restaurant Reviews. Journal of Travel Research, 58(1), 149-167. https://doi.org/10.1177/0047287517744672
Wang, Y., Huang, M., Zhu, X., & Zhao, L. (2016). Attention-based LSTM for Aspect-level Sentiment Classification. https://doi.org/10.18653/v1/D16-1058
Yan, X., Wang, J., & Chau, M. (2015). Customer revisit intention to restaurants: Evidence from online reviews. Information Systems Frontiers, 17(3), 645-657. https://doi.org/10.1007/s10796-013-9446-5
Yu, B., Zhou, J., Zhang, Y., & Cao, Y. (2017). Identifying Restaurant Features via Sentiment Analysis on Yelp Reviews.
Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60(2), 617-663. https://doi.org/10.1007/s10115-018-1236-4
Zhang, Z., Ye, Q., Zhang, Z., & Li, Y. (2011). Sentiment classification of Internet restaurant reviews written in Cantonese. Expert Systems with Applications, 38(6), 7674-7682. https://doi.org/10.1016/j.eswa.2010.12.147
Zuheros, C., Martínez-Cámara, E., Herrera-Viedma, E., & Herrera, F. (2021). Sentiment Analysis based Multi-Person Multi-criteria Decision Making methodology using natural language processing and deep learning for smarter decision aid. Case study of restaurant choice using TripAdvisor reviews. Information Fusion, 68, 22-36. https://doi.org/10.1016/j.inffus.2020.10.019
 
 
Journal of Information Technology Management
Volume 17, Issue 2
2025
Pages 50-68

Files

Share

How to cite

Statistics