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Preprints of the Keldysh Institute of Applied Mathematics, 2021, 050, 14 pp.
DOI: https://doi.org/10.20948/prepr-2021-50
(Mi ipmp2967)
 

This article is cited in 3 scientific papers (total in 3 papers)

Machine learning models for bank reviews classification

N. D. Badanina, V. A. Sudakov
References:
Abstract: Using the banking products and services review corpus, analysis is conducted to establish different text classification models. The paper explores different approaches to the processing of unstructured textual information. Based on the selected approaches, the review corpus on banking products and services received during the COVID-19 pandemic is analyzed. An automatic Internet resources parser has been developed to obtain the required training sample. Software has been developed that implemens basic methods for the classification models construction. This model can be used to create system for monitoring people’s attitudes to banking processes.
Keywords: classification, data analysis, document context, words importance, linguistics, machine learning.
Funding agency Grant number
Russian Foundation for Basic Research 20-51-80002
Document Type: Preprint
Language: Russian
Citation: N. D. Badanina, V. A. Sudakov, “Machine learning models for bank reviews classification”, Keldysh Institute preprints, 2021, 050, 14 pp.
Citation in format AMSBIB
\Bibitem{BadSud21}
\by N.~D.~Badanina, V.~A.~Sudakov
\paper Machine learning models for bank reviews classification
\jour Keldysh Institute preprints
\yr 2021
\papernumber 050
\totalpages 14
\mathnet{http://mi.mathnet.ru/ipmp2967}
\crossref{https://doi.org/10.20948/prepr-2021-50}
Linking options:
  • https://www.mathnet.ru/eng/ipmp2967
  • https://www.mathnet.ru/eng/ipmp/y2021/p50
  • This publication is cited in the following 3 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Препринты Института прикладной математики им. М. В. Келдыша РАН
    Statistics & downloads:
    Abstract page:161
    Full-text PDF :109
    References:21
     
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