Application of natural language processing to automate the analysis of user feedback

Abstract

The purpose of the research is to develop a system for automated analysis of user feedback based on natural language processing methods to identify sentiments, key aspects and themes. The relevance of the research topic is due to the growing need for businesses in effective tools for analyzing large volumes of text data, which would allow extracting valuable insights from user feedback and transforming them into specific recommendations for improving products and services. This topic is of particular importance in the context of e-commerce, services and software development, where the quality and speed of response to user feedback directly affect the competitiveness of companies. Research methods include machine learning, deep neural networks, statistical text analysis methods and methods for assessing the quality of models. Classical algorithms, neural network models, transformer architectures are used. The statistical significance of the results obtained was experimentally confirmed and recommendations for selecting models for various scenarios were developed. The relevance of the research topic is due to the growing business need for effective tools for analyzing large volumes of text data, which would allow extracting valuable insights from user reviews and transforming them into specific recommendations for improving products and services. The research results can be used in e-commerce, service companies, software development, marketing, and analytics to automate review analysis and identify trends in large volumes of text data.