Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce
Autor: | Hüseyin Fidan |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Information retrieval
Text mining business.industry Computer science Grey relational classification 02 engineering and technology E-commerce General Business Management and Accounting Computer Science Applications Weighting Support vector machine Statistical classification Naive Bayes classifier Consumer relationships management Consumer evaluation analysis Categorization Bag-of-words model 020204 information systems 0202 electrical engineering electronic engineering information engineering Vector space model Grey system theory business |
Zdroj: | Journal of theoretical and applied electronic commerce research v.15 n.1 2020 SciELO Chile CONICYT Chile instacron:CONICYT Journal of Theoretical and Applied Electronic Commerce Research Volume 15 Issue 1 Pages 5-65 Journal of theoretical and applied electronic commerce research, Volume: 15, Issue: 1, Pages: 48-65, Published: JAN 2020 |
Popis: | 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. |
Databáze: | OpenAIRE |
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