IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i4p566-d1338428.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Irina Kalabikhina & Vadim Moshkin & Anton Kolotusha & Maksim Kashin & German Klimenko & Zarina Kazbekova, 2024. "Advancing Semantic Classification: A Comprehensive Examination of Machine Learning Techniques in Analyzing Russian-Language Patient Reviews," Mathematics, MDPI, vol. 12(4), pages 1-17, February.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:4:p:566-:d:1338428
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/4/566/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/4/566/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Elvira Ismagilova & Yogesh K. Dwivedi & Emma Slade & Michael D. Williams, 2017. "Electronic Word-of-Mouth (eWOM)," SpringerBriefs in Business, in: Electronic Word of Mouth (eWOM) in the Marketing Context, chapter 0, pages 17-30, Springer.
    2. Vasile-Daniel Păvăloaia & Elena-Mădălina Teodor & Doina Fotache & Magdalena Danileţ, 2019. "Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences," Sustainability, MDPI, vol. 11(16), pages 1-21, August.
    3. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    4. Paul A. Pavlou & Angelika Dimoka, 2006. "The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation," Information Systems Research, INFORMS, vol. 17(4), pages 392-414, December.
    5. Elvira Ismagilova & Yogesh K. Dwivedi & Emma Slade & Michael D. Williams, 2017. "Electronic Word of Mouth (eWOM) in the Marketing Context," SpringerBriefs in Business, Springer, number 978-3-319-52459-7, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nisar, Tahir M. & Prabhakar, Guru & Ilavarasan, P. Vigneswara & Baabdullah, Abdullah M., 2020. "Up the ante: Electronic word of mouth and its effects on firm reputation and performance," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    2. Ismagilova, Elvira & Slade, Emma & Rana, Nripendra P. & Dwivedi, Yogesh K., 2020. "The effect of characteristics of source credibility on consumer behaviour: A meta-analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    3. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    4. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    5. Guha Majumder, Madhumita & Dutta Gupta, Sangita & Paul, Justin, 2022. "Perceived usefulness of online customer reviews: A review mining approach using machine learning & exploratory data analysis," Journal of Business Research, Elsevier, vol. 150(C), pages 147-164.
    6. Fitria Dwi Puspita & Abdul Wahib Muhaimin & Silvana Maulidah, 2024. "Study of Porang Rice Purchase Intention: Moderating Role of Price Sensitivity," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(1), pages 2630-2642, January.
    7. Dominik Gutt, 2018. "In the Eye of the Beholder? Empirically Decomposing Different Economic Implications of the Online Rating Variance," Working Papers Dissertations 40, Paderborn University, Faculty of Business Administration and Economics.
    8. Krishen, Anjala S. & Dwivedi, Yogesh K. & Bindu, N. & Kumar, K. Satheesh, 2021. "A broad overview of interactive digital marketing: A bibliometric network analysis," Journal of Business Research, Elsevier, vol. 131(C), pages 183-195.
    9. Mohammad Al-Khasawneh & Shafig Al-Haddad & Abdel-Aziz Ahmad Sharabati & Hebatallah Hisham Al Khalili & Lana Laith Azar & Farah Waleed Ghabayen & Leen Mazen Jaber & Mariam Husam Ali & Ra’ed Masa’deh, 2023. "How Online Communities Affect Online Community Engagement and Word-of-Mouth Intention," Sustainability, MDPI, vol. 15(15), pages 1-23, August.
    10. Dipankar Das, 2022. "Measurement of Trustworthiness of the Online Reviews," Papers 2210.00815, arXiv.org, revised Nov 2023.
    11. Kuttimani Tamilmani & Nripendra P. Rana & Robin Nunkoo & Vishnupriya Raghavan & Yogesh K. Dwivedi, 2022. "Indian Travellers’ Adoption of Airbnb Platform," Information Systems Frontiers, Springer, vol. 24(1), pages 77-96, February.
    12. Ismagilova, Elvira & Dwivedi, Yogesh K. & Slade, Emma, 2020. "Perceived helpfulness of eWOM: Emotions, fairness and rationality," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    13. Cheng Yi & Zhenhui (Jack) Jiang & Xiuping Li & Xianghua Lu, 2019. "Leveraging User-Generated Content for Product Promotion: The Effects of Firm-Highlighted Reviews," Information Systems Research, INFORMS, vol. 30(3), pages 711-725, September.
    14. Verma, Deepak & Prakash Dewani, Prem & Behl, Abhishek & Pereira, Vijay & Dwivedi, Yogesh & Del Giudice, Manilo, 2023. "A meta-analysis of antecedents and consequences of eWOM credibility: Investigation of moderating role of culture and platform type," Journal of Business Research, Elsevier, vol. 154(C).
    15. Young Kwark & Gene Moo Lee & Paul A. Pavlou & Liangfei Qiu, 2021. "On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data," Information Systems Research, INFORMS, vol. 32(3), pages 895-913, September.
    16. Pradeep Kumar Roy & Zishan Ahmad & Jyoti Prakash Singh & Mohammad Abdallah Ali Alryalat & Nripendra P. Rana & Yogesh K. Dwivedi, 2018. "Finding and Ranking High-Quality Answers in Community Question Answering Sites," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(1), pages 53-68, March.
    17. Roy, Pradeep K. & Singh, Jyoti P. & Baabdullah, Abdullah M. & Kizgin, Hatice & Rana, Nripendra P., 2018. "Identifying reputation collectors in community question answering (CQA) sites: Exploring the dark side of social media," International Journal of Information Management, Elsevier, vol. 42(C), pages 25-35.
    18. Hernández-Ortega, Blanca, 2020. "When the performance comes into play: The influence of positive online consumer reviews on individuals' post-consumption responses," Journal of Business Research, Elsevier, vol. 113(C), pages 422-435.
    19. Jansen, Thomas & Moura, Francisco Tigre, 2024. "WOM, eWOM and WOMachine: The evolution of consumer recommendations through a systematic review of 194 studies," IU Discussion Papers - Marketing & Communication 3 (Juni 2024), IU International University of Applied Sciences.
    20. Tamilmani, Kuttimani & Rana, Nripendra P. & Prakasam, Naveena & Dwivedi, Yogesh K., 2019. "The battle of Brain vs. Heart: A literature review and meta-analysis of “hedonic motivation” use in UTAUT2," International Journal of Information Management, Elsevier, vol. 46(C), pages 222-235.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:4:p:566-:d:1338428. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.