Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce

Abstract: Companies have gained important advantages by the development of electronic commerce. Consumer evaluations in electronic environment offer great possibilities for analysis. The fact that the consumer opinions are comprised of textual data, analyzes have complicated and challenging process....

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Autor principal:	Fidan,Hüseyin
Lenguaje:	English
Publicado:	Universidad de Talca 2020
Materias:	Consumer relationships management Consumer evaluation analysis Text mining Grey system theory Grey relational classification
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762020000100105
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spelling	oai:scielo:S0718-187620200001001052019-12-17Grey Relational Classification of Consumers' Textual Evaluations in E-CommerceFidan,Hüseyin Consumer relationships management Consumer evaluation analysis Text mining Grey system theory Grey relational classification Abstract: Companies have gained important advantages by the development of electronic commerce. Consumer evaluations in electronic environment offer great possibilities for analysis. The fact that the consumer opinions are comprised of textual data, analyzes have complicated and challenging process. In recent years, it is seen that text mining methods are used in analyzes in the literature. However, the evaluations of consumers which are formed by short texts make it necessary to realize the analysis with insufficient data. The weighting methods such as Term Frequency and Term Frequency-Inverse Document Frequency as well as common used classification algorithms such as Naïve Bayes and Support Vector Machine have some inadequacies in short text analysis. In this study, a grey relational classification model based on Vector Space Model and Bag of Words has been developed. The model was first applied to the positive-negative categorization of the evaluations, then, applied to the classification of negative evaluations. It was determined that the accuracy level of the model is higher than the classification algorithms commonly used in short text. According to the results of the research, 9637 negative evaluations in 24479 consumer opinion were determined, and 50.4% of the negative evaluations were found to have the most problems related to product.info:eu-repo/semantics/openAccessUniversidad de TalcaJournal of theoretical and applied electronic commerce research v.15 n.1 20202020-01-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762020000100105en10.4067/S0718-18762020000100105
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Consumer relationships management Consumer evaluation analysis Text mining Grey system theory Grey relational classification
spellingShingle	Consumer relationships management Consumer evaluation analysis Text mining Grey system theory Grey relational classification Fidan,Hüseyin Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce
description	Abstract: Companies have gained important advantages by the development of electronic commerce. Consumer evaluations in electronic environment offer great possibilities for analysis. The fact that the consumer opinions are comprised of textual data, analyzes have complicated and challenging process. In recent years, it is seen that text mining methods are used in analyzes in the literature. However, the evaluations of consumers which are formed by short texts make it necessary to realize the analysis with insufficient data. The weighting methods such as Term Frequency and Term Frequency-Inverse Document Frequency as well as common used classification algorithms such as Naïve Bayes and Support Vector Machine have some inadequacies in short text analysis. In this study, a grey relational classification model based on Vector Space Model and Bag of Words has been developed. The model was first applied to the positive-negative categorization of the evaluations, then, applied to the classification of negative evaluations. It was determined that the accuracy level of the model is higher than the classification algorithms commonly used in short text. According to the results of the research, 9637 negative evaluations in 24479 consumer opinion were determined, and 50.4% of the negative evaluations were found to have the most problems related to product.
author	Fidan,Hüseyin
author_facet	Fidan,Hüseyin
author_sort	Fidan,Hüseyin
title	Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce
title_short	Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce
title_full	Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce
title_fullStr	Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce
title_full_unstemmed	Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce
title_sort	grey relational classification of consumers' textual evaluations in e-commerce
publisher	Universidad de Talca
publishDate	2020
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762020000100105
work_keys_str_mv	AT fidanhuseyin greyrelationalclassificationofconsumerstextualevaluationsinecommerce
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Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce

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