Advancing Semantic Classification: A Comprehensive Examination of Machine Learning Techniques in Analyzing Russian-Language Patient Reviews

Irina Kalabikhina (), Vadim Moshkin, Anton Kolotusha, Maksim Kashin, German Klimenko and Zarina Kazbekova
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Irina Kalabikhina: Population Department, Faculty of Economics, Lomonosov Moscow State University, Moscow 119991, Russia
Vadim Moshkin: Department of Information Systems, Ulyanovsk State Technical University, Ulyanovsk 432027, Russia
Anton Kolotusha: Population Department, Faculty of Economics, Lomonosov Moscow State University, Moscow 119991, Russia
Maksim Kashin: Department of Information Systems, Ulyanovsk State Technical University, Ulyanovsk 432027, Russia
German Klimenko: Population Department, Faculty of Economics, Lomonosov Moscow State University, Moscow 119991, Russia
Zarina Kazbekova: Population Department, Faculty of Economics, Lomonosov Moscow State University, Moscow 119991, Russia

Mathematics, 2024, vol. 12, issue 4, 1-17

Abstract: Currently, direct surveys are used less and less to assess satisfaction with the quality of user services. One of the most effective methods to solve this problem is to extract user attitudes from social media texts using natural language text mining. This approach helps to obtain more objective results by increasing the representativeness and independence of the sample of service consumers being studied. The purpose of this article is to improve existing methods and test a method for classifying Russian-language text reviews of patients about the work of medical institutions and doctors, extracted from social media resources. The authors developed a hybrid method for classifying text reviews about the work of medical institutions and tested machine learning methods using various neural network architectures (GRU, LSTM, CNN) to achieve this goal. More than 60,000 reviews posted by patients on the two most popular doctor review sites in Russia were analysed. Main results: (1) the developed classification algorithm is highly efficient—the best result was shown by the GRU-based architecture (val_accuracy = 0.9271); (2) the application of the method of searching for named entities to text messages after their division made it possible to increase the classification efficiency for each of the classifiers based on the use of artificial neural networks. This study has scientific novelty and practical significance in the field of social and demographic research. To improve the quality of classification, in the future, it is planned to expand the semantic division of the review by object of appeal and sentiment and take into account the resulting fragments separately from each other.

Keywords: machine learning; patient reviews; neural networks; online reviews; review classification; text reviews; quality of medical services; GRU architecture; LSTM; CNN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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