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Study Resources (Business Management)

  Application Case 7.6: A Potpourri of Text Mining Case Synopses 1. What do you think are the common characteristics of the kind of challenges these five companies were facing? 2.What are the types of solution methods and tools proposed in these case synopses? 3.What do you think are the key benefits of using.
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  Section 8.10 Review Questions 1.What is social media analytics? What type of data is analyzed with it? 2.What are the reasons/motivations behind the exponential growth of social media analytics? 3.How can you measure the impact of social media analytics? 4.List and briefly describe the best practices in social media analytics. 5.Why do you think social.
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  ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION?  ?   Application Case 7.1: Text Mining for Patent Analysis 1. Why is it important for companies to keep up with patent filings? 2.How did Kodak use text analytics to better analyze patents? 3.What were the challenges, the proposed solution, and the obtained results?     .
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  3.Discuss the differences and commonalities between text mining and sentiment analysis. 4.In your own words, define text mining and discuss its most popular applications. 5.Discuss the similarities and differences between the data mining process (e.g., CRISP-DM) and the three-step, high-level text mining process explained in this chapter. 6.What does it mean to introduce.
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Section 8.1 Review Questions 1.What does Security First do? 2.What were the main challenges Security First was facing? 3.What was the solution approach? What types of analytics were integrated in the solution? 4.Based on what you learn from the vignette, what do you think are the relationships between Web analytics, text mining, and sentiment.
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  Application Case 7.2: Text Mining Improves Hong Kong Government’s Ability to Anticipate and Address Public Complaints 1. How did the Hong Kong government use text mining to better serve its constituents? 2.What were the challenges, the proposed solution, and the obtained results?     .
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  Application Case 8.3: Understanding Why Customers Abandon Shopping CartsResults in $10 Million Sales Increase 1. How did Lotte.com use analytics to improve sales? 2.What were the challenges, the proposed solution, and the obtained results? 3.Do you think e-commerce companies are in better position to leverage benefits of analytics? Why? How?     .
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  Application Case 7.5: Research Literature Survey with Text Mining 1. How can text mining be used to ease the task of literature review? 2.What are the common outcomes of a text mining project on a specific collection of journal articles? Can you think of other potential outcomes not mentioned in this case?     .
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  18.Describe how special lexicons are used in identification of sentiment polarity. 19.What is speech analytics? How does it relate to sentiment analysis? 20.Describe the acoustic approach to speech analytics. 21.Describe the linguistic approach to speech analytics.     .
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  Application Case 7.4: Text Mining and Sentiment Analysis Help Improve Customer Service Performance 1. How did the financial services firm use text mining and text analytics to improve its customer service performance? 2.What were the challenges, the proposed solution, and the obtained results?     .
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  Application Case 7.7: Whirlpool Achieves Customer Loyalty and Product Success with Text Analytics 1. How did Whirlpool use capabilities of text analytics to better understand their customers and improve product offerings? 2.What were the challenges, the proposed solution, and the obtained results?     .
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  Section 8.7 Review Questions 1.What is a maturity model? 2.List and comment on the six stages of TDWI’s BI maturity framework. 3.What are the six dimensions used in Hamel’s Web analytics maturity model? 4.Describe Attensity’s framework for VOC strategy. List and describe the four stages.     .
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  8.Discuss the expected benefits of Web structure mining. Provide examples from real-world applications that you are familiar with. 9.What is Web usage mining? Draw a picture of the Web usage mining process and explain/discuss the major steps in the process. 10.Provide two exemplary business applications of Web usage mining; discuss their usage.
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  Application Case 7.8: Cutting Through the Confusion: Blue Cross Blue Shield of North Carolina Uses Nexidia’s Speech Analytics to Ease Member Experience in Healthcare   1. For a large company like BCBSNC with a lot of customers, what does “listening to customers” mean? 2.What were the challenges, the proposed solution, and the obtained.
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Section 9.1 Review Questions 1. In what ways were the individual companies in Midwest ISO better off being part of MISO as opposed to operating independently? 2. The dispatch problem was solved with a linear programming method. Explain the need of such method in light of the problem discussed in the case. 3..
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  Section 8.6 Review Questions 1.What are the three types of data generated through Web page visits? 2.What is clickstream analysis? What is it used for? 3.What are the main applications of Web mining? 4.What are commonly used Web analytics metrics? What is the importance of metrics?     .
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  Section 7.8 Review Questions 1.What are the most popular application areas for sentiment analysis? Why? 2.How can sentiment analysis be used for brand management? 3.What would be the expected benefits and beneficiaries of sentiment analysis in politics? 4.How can sentiment analysis be used in predicting financial markets?     .
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  ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION?  ?   Application Case 8.1: Identifying Extremist Groups with Web Link and Content Analysis 1. How can Web link/ content analysis be used to identify extremist groups? 2.What do you think are the challenges and the potential solution to such intelligence gathering activities? Application Case 8.2: IGN Increases.
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  Section 8.2 Review Questions 1.What are some of the main challenges the Web poses for knowledge discovery? 2.What is Web mining? How does it differ from regular data mining or text mining? 3.What are the three main areas of Web mining? 4.Identify three application areas for Web mining (at the bottom of Figure 8.1)..
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  ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS?  ? 1.Describe challenges that APUS was facing. Discuss the ramifications of such challenges. 2.What types of data did APUS tap into? What do you think are the main obstacles one would have to overcome when using data that comes from different domains and sources? 3.What.
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  Section 7.9 Review Questions 1.What are the main steps in carrying out sentiment analysis projects? 2.What are the two common methods for polarity identification? What is the main difference between the two? 3.Describe how special lexicons are used in identification of sentiment polarity.     .
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  Application Case 8.5: Social Network Analysis Helps Telecommunication Firms 1. How can social network analysis be used in the telecommunications industry? 2.What do you think are the key challenges, potential solution, and probable results in applying SNA in telecommunications firms?     .
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  ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION?  ?  ? 1.Explain the relationship among data mining, text mining, and Web mining. 2.What should an organization consider before making a decision to purchase Web mining software? 3.Discuss the differences and commonalities between text mining and Web mining. 4.In your own words, define Web mining and.
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