Modelling and Data Analysis
2024. Vol. 14, no. 1, 7–26
doi:10.17759/mda.2024140101
ISSN: 2219-3758 / 2311-9454 (online)
Semantic Analysis of Reviews About Organizations Using Machine Learning Methods
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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Zellig S. H. Distributional Structure // Word. 2015 Vol. 10. №2. P.146-162. DOI: 10.1080/00437956.1954.11659520
- 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
- 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
- Mikolov T., Chen K., Corrado G., Dean J. Efficient Estimation of Word Representations in Vector Space // arXiv preprint arXiv: 3781.2013
- Mikolov T., Quoc V. Le, Sutskever I. Exploiting Similarities among Languages for Machine Translation // arXiv preprint arXiv: 4168v1.2013
- 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
- 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
- Hochreiter, S., Schmidhuber, J. Long short-term memory // Neural Computation. 1997. Vol. № 8. P.1735–1780. DOI: 10.1162/neco.1997.9.8.1735
- 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
- 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
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