Week 6 assignment Data mining.docx ITS-632 WeeK 6 University of the Cumberlands Data Mining (ITS-632-M22)-Full Term Question 1: The goal of Hemmatian2019 was to create a thorough survey by assessing well-known ex
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Week 6 assignment Data mining.docx ITS-632 WeeK 6 University of the Cumberlands Data Mining (ITS-632-M22)-Full Term Question 1: The goal of Hemmatian2019 was to create a thorough survey by assessing well-known existing methodologies in opinion mining as well as their limitations. Supervised techniques have been described as an appropriate model for accurately and validly classifying comments. Nonetheless, because they rely on labeled training materials, they follow a slow and expensive trend. Furthermore, semi-supervised approaches are gaining popularity, and considering the prevalence of microblogs like Twitter, semi-supervised approaches are good candidates for microblogs. Clustering and lexicon-based approaches are becoming increasingly popular among researchers, (Hemmatian 2019). Question 2: Opinion mining, also known as sentiment analysis, is a method for automatically extracting knowledge or information from user reviews on a certain topic, problem, or product. Because there are so many resources available nowadays, extracting knowledge from the World Wide Web is quite difficult. It is beneficial to categorize each and every point of view in terms of the business. For instance, ordering or trustworthiness, as well as product quality. Business intelligence uses the entire pinion mining process. Opinion mining is a very useful feature in deciding new strategies and schemes. The main and most significant task of opinion mining is to examine people's opinions and draw conclusions about the character. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . .. .. . . . . . . . . . . . . . . . . . .. . . . .
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