Semantic Analysis of Reviews About Organizations Using Machine Learning Methods

48

Abstract

Semantic analysis of organizational reviews is a key tool for assessing customer satisfaction levels. Business entities should regularly conduct analysis and emotional sentiment investigation to delve deeper into the data and gain a more comprehensive understanding of their operations, including through the use of machine learning methods. Presently, deep learning-based methods are garnering increased attention due to their high efficiency. In this study, we will focus on sentiment analysis tasks. To perform sentiment analysis, we will employ machine learning methods, including various approaches to text vectorization, deep learning models, and natural language processing (NLP) algorithms.

General Information

Keywords: natural language processing, classification task, gradient boosting, recurrent neural networks, convolutional neural networks, BERT, GPT

Journal rubric: Data Analysis

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2024140101

Received: 26.02.2024

Accepted:

For citation: Platonov E.N., Martynova I.R. Semantic Analysis of Reviews About Organizations Using Machine Learning Methods. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2024. Vol. 14, no. 1, pp. 7–26. DOI: 10.17759/mda.2024140101. (In Russ., аbstr. in Engl.)

References

  1. Anjomshoaa H., Snagui Moharer R., Shirazi M. The effectiveness of training based on neuro-linguistic programming and cognitive-behavioral approach on students' anxiety, depression, and stress // International Journal of Pediatrics. 2021.9. P.14856–14866. DOI: 10.22038/IJP.2021.57871.4539
  2. Begum A. J., Paulraj I. J. M., Banu S. H. (2022). Neuro-linguistic programming (NLP) is a promising communicative English language teaching technique // Sch Int J Linguist Lit. Vol.5. P.100- DOI:10.36348/sijll.2022.v05i03.004
  3. Corcoran, C.M., Mittal, V.A., Bearden, C.E., E. Gur, R., Hitczenko, K., Bilgrami, Z., Savic, A., Cecchi, G.A., Wolff, P. Language as a biomarker for psychosis: a natural language processing approach // Schizophr. 2020. Vol. 226. P. 158–166. DOI: 10.1016/j.schres.2020.04.032
  4. Chengyi Z., Brian Z.H., Andranik A.A., Beth C., Natural Language Processing to Identify Pulmonary Nodules and Extract Nodule Characteristics from Radiology Reports // Chest. 2021. Vol. 160. №4. P.1902-1914. DOI: 10.1016/j.chest.2021.05.048
  5. Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: a survey // Ain Shams Engineering Journal. 2014. Vol. 5. №1. P.1093-1113. DOI: 10.1016/j.asej.2014.04.011
  6. Onan A. Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks // Concurrency Comput. Pract. 2020. Vol. 33. P. 814-833. DOI: 1002/cpe.5909
  7. Karthik R.V., Ganapathy S. A fuzzy recommendation system for predicting customers' interests using sentiment analysis and ontology in e-commerce // Applied Soft Computing. 2021. Vol. 108. DOI:10.1016/j.asoc.2021.107396
  8. Bose R., Dey R.K., Roy S., Sarddar D. Sentiment analysis on online product reviews // Information and Communication Technology for Sustainable Development. 2020. Vol.993 P. 559-569. DOI:10.1007/978-981-13-7166-0_56
  9. Mai L, Le B Joint sentence and aspect-level sentiment analysis of product comments // Annals of Operations Research. 2021. 300. №2. P.493–513. DOI: 10.1007/s10479-020-03534-7
  10. Zhang Y., Du J., Ma X., Wen H., Fortino G. Aspect-based sentiment analysis for user reviews // Cognitive Computation. 2021. Vol. 13. №5. P. 1114-1127. DOI: 10.1007/s12559-021-09855-4
  11. Zellig S. H. Distributional Structure // Word. 2015 Vol. 10. №2. P.146-162. DOI: 10.1080/00437956.1954.11659520
  12. Li Y., Yang T. Word Embedding for Understanding Natural Language: A Survey. In: Srinivasan S. (eds) Guide to Big Data Applications. Studies in Big Data. 2017. Vol 26. DOI: 10.1007/978–3-319–53817–4_4
  13. Sparck K.J. A statistical interpretation of term specificity and its application in retrieval // Journal of Documentation. 1972. Vol. 28 № 1. P. 11-21. DOI: 10.1108/eb026526
  14. Mikolov T., Chen K., Corrado G., Dean J. Efficient Estimation of Word Representations in Vector Space // arXiv preprint arXiv:  3781.2013
  15. Mikolov T., Quoc V. Le, Sutskever I. Exploiting Similarities among Languages for Machine Translation // arXiv preprint arXiv: 4168v1.2013
  16. Dorogush A.V., Ershov V., Gulin A. CatBoost: gradient boosting with categorical features support // Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018. P. 6639–6649
  17. LeCun Y., Boser B., Denker J. S., Henderson D., Howard R. E., Hubbard W., Jackel L. D. Backpropagation Applied to Handwritten Zip Code Recognition. // Neural Computation. Vol. 1 №4. P.541–551. DOI: 10.1162/neco.1989.1.4.541
  18. Hochreiter, S., Schmidhuber, J. Long short-term memory // Neural Computation. 1997. Vol. № 8. P.1735–1780. DOI: 10.1162/neco.1997.9.8.1735
  19. Vaswani A., Shazeer N., Parmar N., J. Uszkoreit, L. Jones, A. Gomez, A. Kaiser, I. Polosukhin. Advances in neural information processing systems. 2017. P. 5998-6008
  20. Liu H., Li Z., Hall D., Liang P., Ma T. Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training//// arXiv preprint arXiv:14342. 2023. URL: https://arxiv.org/abs/2305.14342

Information About the Authors

Evgeniy N. Platonov, PhD in Physics and Matematics, Assistant Professor, Moscow Aviation Institute (National Research University), Moscow, Russia, ORCID: https://orcid.org/0000-0001-8502-1350, e-mail: en.platonov@gmail.com

Irina Martynova, Student of the Institute of Information Technologies and Applied Mathematics, Moscow Aviation Institute (National Research University), Moscow, Russia, ORCID: https://orcid.org/0009-0007-3140-2490, e-mail: irina.mart.r@gmail.com

Metrics

Views

Total: 124
Previous month: 19
Current month: 4

Downloads

Total: 48
Previous month: 5
Current month: 0