Method of Cross-Language Aspect-Oriented Analysis of Statements Using Categorization Model of Machine Learning.

Kovaliuk, Tetiana and Tielysheva, Tamara and Kobets, Nataliya (2019) Method of Cross-Language Aspect-Oriented Analysis of Statements Using Categorization Model of Machine Learning. CEUR Workshop Proceedings Volume 2362, 2019 3rd International Conference on Computational Linguistics and Intelligent Systems, COLINS 2019; Kharkiv; Ukraine; 18 April 2019 до 19 April 2019, 2362. pp. 32-42. ISSN 1613-0073

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Abstract

Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Today, the Internet has become the largest source of consumer thought. Sentiment analysis and opinion mining is the field of study that analyzes people’s opinions, sentiments, evaluations, attitudes, and emotions from written language. In this paper, we present a study of aspect-based opinion mining using a lexicon-based approach and their adaptation to the processing of responses written in Ukrainian and English. This information helps to build systems to understand customer’s feedback and plan business strategies accordingly. This also helps in predicting the chances of product failure. In this paper, it is explained how machine learning can be used for opinion mining. The research methods used in the work are based on data mining methods, Web mining, machine learning, and information retrieval. The stages of the method of cross-language aspect-oriented analysis of statements are presented. The cross-language categorization of characteristics of goods is considered. The algorithm describes the model learning in cross-language virtual contextual documents.

Item Type: Article
Additional Information: URL статті: http://ceur-ws.org/Vol-2362/paper4.pdf
Uncontrolled Keywords: analysis of opinion, review, aspect; opinion orientation; sentiment analysis; categorization; machine learning
Subjects: Статті у наукометричних базах > Scopus
Divisions: Факультети > Факультет інформаційних технологій та управління > Кафедра комп'ютерних наук і математики
Depositing User: Наталія Михайлівна Кобець
Date Deposited: 24 Jun 2019 08:05
Last Modified: 17 Oct 2019 12:38
URI: http://elibrary.kubg.edu.ua/id/eprint/27658

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