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22302961 Case Study And Discussion

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University of Kentucky
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Running head: DISCUSSION QUESTION AND CASE STUDY 1
Discussion Question and Case Study
Student’s Name
Institution Affiliation

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DISCUSSION QUESTION AND CASE STUDY 2
Discussion Question and Case Study
1. Explain the relationship between data mining, text mining, and sentiment analysis.
Data mining is usually a procedure dependent on algorithms to analyze and retrieve vital
information from data. Text mining is a procedure that requires changing unformed text
resources into relevant, organized data. Sentiment analysis is the explanation and categorization
of feelings within text data using various techniques. The relationship among them is that they
are all used to enhance the making of proper decisions, minimize costs, and enhance business
activities like strategic planning through data analysis.
2. In your own words, define text mining, and discuss its most popular applications.
Text mining entails applications of data mining to unorganized data through analyzing words.
Unstructured data is increasing rapidly hence, the use of text mining has grown. Using
significant techniques like sentiment analysis, text mining can obtain vital information from data
and knowledge, which is hidden in text content. One of its most popular applications is in risk
management (Dang, 2014). Lacking risk analysis in business usually leads to failure. Hence, text
mining is used through the adoption of risk management software, which is generally based on
text mining to enhance the capability to mitigate risks. Text mining can also be used in the
customer care services by making sure the customers are provided with an automated feedback
whenever they have issues, which need to be resolved.
3. What does it mean to induce structure into text-based data? Discuss the alternative
ways of inducing structure into them.

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Running head: DISCUSSION QUESTION AND CASE STUDY Discussion Question and Case Study Student’s Name Institution Affiliation 1 DISCUSSION QUESTION AND CASE STUDY Discussion Question and Case Study 1. Explain the relationship between data mining, text mining, and sentiment analysis. Data mining is usually a procedure dependent on algorithms to analyze and retrieve vital information from data. Text mining is a procedure that requires changing unformed text resources into relevant, organized data. Sentiment analysis is the explanation and categorization of feelings within text data using various techniques. The relationship among them is that they are all used to enhance the making of proper decisions, minimize costs, and enhance business activities like strategic planning through data analysis. 2. In your own words, define text mining, and discuss its most popular applications. Text mining entails applications of data mining to unorganized data through analyzing words. Unstructured data is increasing rapidly hence, the use of text mining has grown. Using significant techniques like sentiment analysis, text mining can obtain vital information from data and knowledge, which is hidden in text content. One of its most popular applications is in risk management (Dang, 2014). Lacking risk analysis in business usually leads to failure. Hence, text mining is used through the adoption of risk management software, which is generally based on text mining to enhance the capability t ...
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