What is text mining? How does it differ from data mining?



1. Question 1. (50 Points) Answer the following questions about Text Analytics. 

a. (10 Points) What is text mining? How does it differ from data mining? 

b. (15 Points) What are some popular application areas of text mining? 

c. (15 Points) What is Web analytics? What are the metrics used in Web analytics? 

d. (10 Points) What is social media analytics? How is it done? Who does it? What comes out of it?

2. Question 2. (25 Points) TelCo, a major telecommunications firm, wants to investigate its problem with customer attrition, or “churn”. They can product relevant customer data and develop focused customer retention programs. Business analysts suggest the Telcom conduct a predictive analysis using decision tree model on the churn dataset with SAS Enterprise Miner (Customer dataset provided in the attachment on Canvas). Specifically, the target variable of the model is the attribute “Churn”. You will need to set the “Churn” variable as “Target” role. In terms of the analysis results, please answer the following questions.

a. (10 Points) In the variable importance table, please list the top three variables which are more informative and could help Telco identify the customers who will churn in the next month. 

b. (10 Points) After running the analysis and generating the results, you will see the output page. In the page, you can check out the event classification table. According to these models’ measures (False Negative, False Positive, True Negative, and True Positive), please calculate the measures of Precision and Recall for the performance of the model. 

c. (5 Points) Please provide a tree map which is generated in the Results panel.

3. Question 3. (25 Points) You are owing a supermarket mall and through membership cards, you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. Spending Score is something you assign to the customer based on your defined parameters like customer behavior and purchasing data. Now you want to understand the customers like who can be easily converge [Target Customers] so that the sense can be given to marketing team and plan the strategy accordingly. In order to answer the question, you must use clustering technology to segment the customers. Data Scientists suggest you use SAS Enterprise Miner to conduct the clustering analysis. The dataset has been provided on Canvas. In order to generate the meaningful customer segments, you can try different cluster numbers such as 2, 3, 4, and 5. According the results, please answer the following questions.

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