Text Categorization Based on Condensed Graph

Abstract

The resonant world events of2020 led to an increase in the amount of information on the Internet, including criminal, fake news, and fake negative reviews. False negative information can spread very quickly, and methods are needed to suppress this process. The development of effective algorithms for automatic text analysis is especially relevant today. The most important subtasks include thematic catesorization, sentiment analysis, includins ABSA (aspect-based sentiment analysis). The paper proposes a combined semantic-statistical alsorithm for the aspect analysis of larse texts, based on the use of a semantic graph. The aspect extraction method contains the phases of selectins a set of sisnificant words, calculatins the weishts of the vertices of the semantic sraph by the relaxation method, filterins aspects based on the sradient method. The method proposed allows to extract domain-dependent aspect terms from trainins data. Different aspect term sets extracted from different domains have the same statistical features, and in the same time lexical diversity and structure are taken into account.