Análisis de sentimientos orientado al comercio electrónico de CITMATEL: proyecto de voz del cliente

Carlos Mar Rodríguez; Patricia  Montañez Castelo; Alfredo Simón Cuevas

Authors

Carlos Mar Rodríguez Empresa de Tecnologías y Servicios Telemáticos - CITMATEL
Patricia Montañez Castelo Technological University of Havana "José Antonio Echeverría"
Alfredo Simón Cuevas Technological University of Havana "José Antonio Echeverría"

Keywords:

e-commerce; natural language processing; sentiment analysis; Voice of the Customer

Abstract

E-commerce plays a key role in digital transformation programs. The extensive use of social networks has allowed customers and consumers to freely express their opinions about products and services, and to express their emotions and experiences, which has had a great impact on online sales businesses, hence the emergence of what is known as Voice of the Customer (VoC). The efficient processing and intelligent analysis of the entire volume of unstructured textual information generated for capturing the user-satisfaction degree is a challenging task, and demands the application of Natural Language Processing technologies, specifically, Sentiment Analysis. This work constitutes a first approach of the Sentiment Analysis technologies in e-commerce developed by CITMATEL. A model of computational processing and analysis of textual information that guides the implementation of a VoC project, with impact on e-commerce, is presented. As part of this model, a concrete Sentiment Analysis solution is developed, whose partial evaluations on a test dataset of product reviews where were obtained high precision and recall which makes it suitable for deployment in the enterprise.

References

Aboelela, E. M., Gad, W., & Ismail, R. (2021). The impact of semantics on aspect level opinion mining. PeerJ Computer Science, 7, pp. 1-22.

Akhoundzade, R., & Devin, K. H. (2020). Unsupervised aspect-based Sentiment Analysis in the Persian language: Extracting and clustering aspects. In Proceedings of the 10th International Bawack, E. B., Wamba, S. F., Carillo, K. D. A, & Akter, S (2022). Artificial intelligence in E-Commerce: a bibliometric study and literature review. Electron Mark, 32(1): 297-338.

Brauwers, G. & Frasincar, F. (2021). A Survey on Aspect-Based Sentiment Classification. ACM Computing Surveys, 1(1).

García-Pablos, A., Cuadros, M. & Rigau, G. (2018). W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis. Expert Systems with Applications, 91: 127-137.

Geetha, M. P., & Karthika Renuka, D. (2021). Improving the performance of aspect based sentiment analysis using fine-tuned Bert Base Uncased model. International Journal of Intelligent Networks, 2, pp. 64-69.

Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 168-177, Seattle, Washington, USA.

Hutto, C., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. In Proceedings of the International AAAI Conference on Web and Social Media, 8(1): 216-225, AAAI Press.

Kian, R. (2021). Provide a model for an e-commerce system with the impact of artificial intelligence. International Journal of Innovation in Management, Economics and Social Sciences, 1(3): 88-94.

Mehtab, A. M. (2021). Sentiment Analysis in E-Commerce. URL: https://medium.com/federatedai/sentiment-analysis-in-e-commerce-e8a06a498a75. [consultado: 1 de diciembre de 2022]

Liu, B. & L. Zhang. (2012). A Survey of Opinion Mining and Sentiment Analysis. In Mining Text Data, C. C. Aggarwal and C. Zhai (Eds). Springer US: Boston, MA, pp. 415-463.

Liu, N., Shen, B., Zhang, Z., Zhang, Z., & Mi, K. (2019). Attention-based Sentiment Reasoner for aspect-based sentiment analysis. Human-centric Computing and Information Sciences, 9(35): 1-17.

López, D. & Arcos, L. (2019). Deep learning for aspect extraction in textual opinions. Revista Cubana de Ciencias Informáticas, 13(2): 105-145.

Nazir, A., Y. Rao, L. Wu, & L. Sun, (2022). Issues and Challenges of Aspect-based Sentiment Analysis: A Comprehensive Survey. IEEE Transactions on Affective Computing, 13(2): 845-863.

Phan, M. H., & Ogunbona, P. (2020) Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3211-3220, Seattle, Washington, USA.

Saraswat, A., Kumar Azad, H., & Abhishek, K. (2022). Towards improving e-commerce customer review analysis for sentiment detection. Scientific Reports, 12(1): 1-15.

Sarkar, D. (2019). Text Analytics with Python. A Practitioner’s Guide to Natural Language Processing, 2nd ed. APress.

Shahzad, K., Pervaz, I., & Nawab, A. (2018). WordNet-based Semantic Similarity Measures for Process Model Matching. In Proceedings of the 17th International Conference on Perspectives in Business Informatics Research. Stockholm, Sweden, pp. 33-44.

Subhashini, L. D. C. S., Li, Y., Zhang, J., Atukorale, A. S., & Wu, Y. (2021). Mining and classifying customer reviews: a survey. Artificial Intelligence Review, 54(8): 6343-6389.

Varathan, K., A. Giachanou, & F. Crestani. (2017). Comparative Opinion Mining: A Review. Journal of the Association for Information Science and Technology, 68(4): 811-829.

Conference on Computer and Knowledge Engineering (ICCKE), pp. 94-100. Mashhad, Iran.