Electronic Recommendation Based on Customer Review Using  Text Mining

Saravana Balaji B; Eman Sabah Mustafa

doi:10.25212/lfu.qzj.6.4.31

Authors

Saravana Balaji B Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region, Iraq.
Eman Sabah Mustafa Master Student, Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region, Iraq.

DOI:

https://doi.org/10.25212/lfu.qzj.6.4.31

Keywords:

Customer Review, NLP (Natural Language Processing), Python, Anaconda, Recommender Systems, Context-aware recommender systems, social networking.

Abstract

In this paper, buyers are looking for reviews of the products before purchasing them. In view of this, online shopping platforms are encouraging their customers to provide reviews on products that would help future customers and the service provider to enhance their services. These reviews are normally in natural language mostly in the English language. These are used to analyze and provide data that is used for repairing and building new products because most services are unable to review consumers' reviews at the same time regularly, so they need mining tools to learn about those reviewers, which is what consumers need for their goods. Users review assessments for upgrading their products. Reviews are analyzed by customers to decide whether to purchase or not to purchase. The main objectives of this study are to develop a recommendation system based on customer reviews, develop a dataset of customer reviews from Amazon and eBay, analyze the reviews to create a database of products, develop an algorithm for generating positive/negative scores for a product, develop a method for gathering user requirements in natural language and to identify the main product, and also develop an algorithm to match the user request with the product and generate recommendations. The study concluded that the selected program was suitable for analyzing and managing the review data.

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References

Al-Ghuribi, S. M., Noah, S. A. M., & Tiun, S. (2020). Unsupervised Semantic Approach of Aspect-Based Sentiment Analysis for Large-Scale User Reviews. IEEE Access, 8, 218592-218613.

Amato, F., Balzano, W., Cozzolino, G., De Luca, A., & Moscato, F. (2019, March). Textual Processing in Social Network Analysis. In Workshops of the International Conference on Advanced Information Networking and Applications (pp. 822-832). Springer, Cham.

Das, P., Jamader, A. R., Acharya, B. R., & Das, H. (2019, May). HMF Based QoS aware Recommended Resource Allocation System in Mobile Edge Computing for IoT. In 2019 International Conference on Intelligent Computing and Control Systems (ICCS) (pp. 444-449). IEEE.

Farsi, M., & Erkoyuncu, J. A. (2021). An agent-based approach to quantify the uncertainty in Product-Service System contract decisions: A case study in the machine tool industry. International Journal of Production Economics, 233, 108014.

García-Sánchez, F., Colomo-Palacios, R., & Valencia-García, R. (2020). A social-semantic recommender system for advertisements. Information Processing & Management, 57(2), 102153.

Ghasemi, N., & Momtazi, S. (2021). Neural text similarity of user reviews for improving collaborative filtering recommender systems. Electronic Commerce Research and Applications, 45, 101019.

Gonsalves, B., & Patil, V. (2017, March). LoQoS location and QoS sensitive web service recommender. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1-4). IEEE.

Hashim, E. W. A., Hammood, M. O., & Al-azraqe, M. T. I. (2016). A Cloud Computing System Based Laborites’ Learning Universities: Case Study of Bayan University’s Laborites-Erbil. Book of Proceeding, 538.

Kamaran faraj. (2014). E-recruitment Tiered Architecture in Feasibility Study Role”. International Journal of Computer Sciences and Engineering ,2015.

Kamaran faraj. (2016). Web-based Teaching in Particular Developing Counties, Experience at “Sulamani University”. International Journal of Computer Sciences and Engineering Vol.-4(3), PP(01-04) Mar 2016, E-ISSN: 2347-2693.

Kh, T., & Hamarash, I. (2020). Model-Based Quality Assessment of Internet of Things Software Applications: A Systematic Mapping Study.

Li, S., Wen, J., Luo, F., Gao, M., Zeng, J., & Dong, Z. Y. (2017). A new QoS-aware web service recommendation system based on contextual feature recognition at server-side. IEEE transactions on network and service management, 14(2), 332-342.

Li, Zhen, and Aoi Shimizu. "Impact of online customer reviews on sales outcomes: An empirical study based on prospect theory." The Review of Socionetwork Strategies 12.2 (2018): 135-151.

Manikandan, V., Gowsic, K., Prince, T., Umamaheswari, R., Ibrahim, B. F., & Sampathkumar, A. (2020, September). DRCNN-IDS Approach for Intelligent Intrusion Detection System. In 2020 International Conference on Computing and Information Technology (ICCIT-1441) (pp. 1-4). IEEE.

Marchand, A., & Marx, P. (2020). Automated product recommendations with preference-based explanations. Journal of retailing, 96(3), 328-343.

Miran, A., & Kadir, G. (2019c). Enhancing AODV routing protocol to support QoS. International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), 8(5), 1824–1830.

Mohammed, A. G. (2021). A Study of Scheduling Algorithms in LTE-Advanced HetNet. QALAAI ZANIST SCIENTIFIC JOURNAL, 6(3), 945-968.

Mohammed, A. S., Andrii, S., Ahmed, A. N., Olha, D., & Owaid, S. R. (2019, December). Multiparametric Assesment of the Conditional Channel of Multitarent Radio Communication Systems using Fuzzy Sets. In 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) (pp. 374-380). IEEE.

Mohanty, B. K., & Passi, K. (2010). Agent based e-commerce systems that react to buyers’ feedbacks–A fuzzy approach. International Journal of Approximate Reasoning, 51(8), 948-963.

Pratik Shukla, Roberto Iriondo 2021. Natural Language Processing (NLP) with Python. Tutorial on the basics of natural language processing (NLP) with sample coding implementations in Python. (pp. 11-12). IEEE.

Qasim, A. J., Din, R., & Alyousuf, F. Q. A. (2020). Review on techniques and file formats of image compression. Bulletin of Electrical Engineering and Informatics, 9(2), 602-610.

Sassi, I. B., Mellouli, S., & Yahia, S. B. (2017). Context-aware recommender systems in mobile environment: On the road of future research. Information Systems, 72, 27-61.

Selmi, A., Brahmi, Z., & Gammoudi, M. M. (2017, June). PACT: A new trust prediction method for multi-agents recommender systems. In 2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) (pp. 9-14). IEEE.

Sharma, A., & Kumar, S. (2021). Network Slicing and the Role of 5G in IoT Applications. In Evolution of Software-Defined Networking Foundations for IoT and 5G Mobile Networks (pp. 172-190). IGI Global.

Shen, L., Liu, Q., Chen, G., & Ji, S. (2020). Text-based price recommendation system for online rental houses. Big Data Mining and Analytics, 3(2), 143-152.

Shi, Y., Lin, H., & Li, Y. (2017, August). Context-aware recommender systems based on item-grain context clustering. In Australasian Joint Conference on Artificial Intelligence (pp. 3-13). Springer, Cham.

Tewari, A. S. (2020). Generating items recommendations by fusing content and user-item based collaborative filtering. Procedia Computer Science, 167, 1934-1940.

Tripathi, A., Ashwin, T. S., & Guddeti, R. M. R. (2019). EmoWare: A context-aware framework for personalized video recommendation using affective video sequences. IEEE Access, 7, 51185-51200.

Walek, B., & Fojtik, V. (2020). A hybrid recommender system for recommending relevant movies using an expert system. Expert Systems with Applications, 158, 113452.

Wu, B., & Ye, Y. (2020). BSPR: Basket-sensitive personalized ranking for product recommendation. Information Sciences, 541, 185-206.