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Analyze quality of products in e-commerce systems with sentimental analysis

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dc.contributor.advisor Premaratne S
dc.contributor.author Bandara PMAU
dc.date.accessioned 2019
dc.date.available 2019
dc.date.issued 2019
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/15844
dc.description.abstract E-commerce websites getting extra significant and popular today since the vast differentiated and diversified information that is presented. Studies says that more than 80% of the world population is using these websites to purchase goods and services online. For these online customers, comments / feedbacks play a major role in decision making when buying the products from the market space. Hence the diversity and the popularity of the Online space, sales of these online products get increased with time. Therefore, it is not practical to review all the given product feedback and come to conclusion on purchasing the product for a consumer. Focusing on this, this study is urges to observe the success factors of online websites and how those aspects influence on online marketing to sales growth in any organization. Therefore, in this research a Analyze Quality of Products in E-commerce System is focused on analyzing the online consumers feedbacks or comments on various products using data mining techniques such as Sentimental and filtering analysis. The outcome from the study will show feature wise relativeness in the mobile phone domain. All procedures were based on the features extracted through a thorough literature review and existing apparatuses. This will aid to calculate a “Trust Score” for the online products and a general overview to achieve a higher trust score for e-commerce organization. en_US
dc.language.iso en en_US
dc.subject INFORMATION TECHNOLOGY-Dissertations en_US
dc.subject ELECTRONIC COMMERCE-Quality Control en_US
dc.subject DATA MINING en_US
dc.subject INTERNET MARKETING-Quality Control en_US
dc.title Analyze quality of products in e-commerce systems with sentimental analysis en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty IT en_US
dc.identifier.degree MSc in Information Technology en_US
dc.identifier.department Department of Information Technology en_US
dc.date.accept 2019
dc.identifier.accno TH3924 en_US


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