COMPARATIVE ANALYSIS OF THEMATIC MODELING METHODS FOR ANALYSIS OF REVIEWS IN THE ONLINE STORE OF DIGITAL GOODS

Abstract

Nowadays, people often use online services for their daily tasks. The Internet has increased the demand for applications and services to provide a better customer experience. However, nowadays the Internet is full of information that can make it difficult to understand customer needs and confuse users when searching for the information they need. Therefore, there is a need to use effective methods and tools that can help in identifying and analyzing information from a large number of sources stored as online text. For such tasks, it is convenient to use natural language processing – an industry that combines the capabilities of computational linguistics, computer science and artificial intelligence to allow computer to understand and analyze meaning of human speech. One of the fundamental tasks of natural language processing is the definition of keywords. Identified keywords are used to determine the needs of users of the product when it comes to analyzing product reviews, and quickly find information about the product by the average user. Topic modeling methods are often used to determine keywords in the text This study provides a comparative analysis of topic modeling methods for use in text documents taken from reviews of digital products in the online store. Topic modeling is an unsupervised machine learning technique that allows you to analyse collection of documents and divide them into different topics. Three of the most popular topic modeling methods presented in this paper for document research are latent semantic analysis LSA, probabilistic latent semantic analysis PLSA, and latent Dirichlet allocation LDA. Comparative analysis is performed using numerical metrics such as coherence, perplexity and “human eye” evaluation metrics using word cloud visualization of results for different parameters of these methods. In addition, a comparison of performance methods was performed.