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Analyzing Customers' Reviews in Online Businesses and their Impact on Product Sales

Ezzati, Farzane | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53434 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Majid, Rafiee
  7. Abstract:
  8. In recent years, the attention of marketing researchers has shifted from numerical product rating to user-generated content. Because of this, customers' online reviews now play a very important role in the destiny of Internet businesses. Today, huge and comprehensive platforms have been developed to record and analyze online customer reviews. This type of unstructured data that users and Internet shoppers create based on their experiences of using products and services, has a significant impact on gaining and losing the trust of other users. After reading each review, each user can express their opinion about the usefulness of that comment, which can be seen by others. Large Internet businesses typically provide a system for sorting reviews so that reviews with a high number of useful comments are displayed sooner than other reviews. Among the tools used today to analyze unstructured textual data, text mining and sentiment analysis are used significantly. In this study, we intend to use these tools to estimate the usefulness of online reviews of Amazon electronic hedonic products. The main purpose of this study is to find the type and intensity of the relationship between NOH and the variables of review sentimental state and subjectivity of reviews. Using the subjectivity of reviews with the help of fuzzy c-means clustering algorithm is a new method that is less used in studies estimating the usefulness of reviews. The results of this study show that the sentimental state of a review will have a positive effect on NOH. Also, certain aspects of the products have been identified by the clustering phase, which indicates that it can have a positive effect on the number of useful votes of a review
  9. Keywords:
  10. Text Mining ; Sentiment Analysis ; Clustering ; Linear Regression ; Online Store ; Online Product Reviews ; Online Businesses

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