Development and Comparative Analsys of Classification Algorithms of Uzbek Taxpayer Complaints and Questions

This paper addresses the problem of classifying taxpayer requests (complaints and suggestions) received by regulatory authorities at two levels: (1) determining the text type (complaint/suggestion) and (2) assigning the complaint to one of five pre-defined categories (Tax Calculation Error, IT Problems, Benefits/Deductions, Procedural Issues, Other). The study proposed two approaches: a rule-oriented (dictionary) method and a combination of machine learning algorithms. The rule-oriented technique is based on pre-compiled dictionaries of keywords and phrases, allowing for quick identification of requests based on a set of “trigger” lexemes. However, testing on real data showed that this technique in some cases (especially with ambiguous text) demonstrates reduced accuracy and completeness. To improve the results, a system was developed in which Naïve Bayes solves the problem of binary classification “offer/complaint”, and the support vector machine classifies any text (complaint or offer) into one of five topics. Training was carried out on a corpus of 2000 texts marked by experts. The testing results confirmed that machine-learning algorithms are superior to dictionary methods: Naïve Bayes gives F1 up to 88-90% when determining “offer/complaint”, and Support vector machine distributes complaints into categories with an accuracy of 90-92%. The obtained results and comparative analysis show that the combination of Naïve Bayes and Support Vector Machine allows achieving high efficiency in processing various requests, while maintaining clear operating logic and interpretability at the binary classifier stage.