Programming
UOTC Data Mining Text Mining and Sentiment Analysis Discussion

University of the Cumberlands

Question Description

Chapter 7 –discussion question #1: Explain the relationship among data mining, text mining and sentiment analysis.

Chapter 7 –discussion question #2: In your own words, define text mining, and discuss its most popular applications.

Chapter 7 –discussion question #3: What does it mean to induce structure into text-based data ? Discuss the alternative ways of including structure into them.

Chapter 7 –discussion question #4: What is the role of NLP in text mining ? Discuss the capabilities and limitations of NLP in the context of text mining.

Chapter 7 –exercise 3: Go to teradatauniversitynetwork.com and find the case study named "eBay Analytics". Read the case carefully and extend your understanding of it by searching the internet for additional information, and answer the case questions. (Case questions are mentioned below)

Chapter 7 Internet Exercise 7: Go to kdnuggets.com. Explore the sections on applications as well as software. Find names of at least three additional packages for data mining in a creative way ?


Exercise 3 Case Questions (4 Questions):

1: How could faster and cheaper experimentation result in more innovation at eBay?

2: Could there be a point at which too much experimentation will be counter-productive?

3: If internal infrastructure support for eBay’s Analytics as a Service (AssS) results in solid business value, could one assume than an outsourced, cloud-based AaaS implementation would perform similarly?

4: Do you see any issues that might be forthcoming around the self-service facility for creating virtual data marts?


Question: What are the common challenges with which sentiment analysis deals? What are the most popular application areas for sentiment analysis? Why? Your response should be 250-300 words.


Final Answer

These parts are ready..

Running Head: CHAPTER 7- DISCUSSION QUESTION #3

Chapter 7 –discussion question #3:
Inducing Structure into text-based data
Student’s Name
Professor
Course
Date

1

INDUCING STRUCTURE INTO TEXT-BASED DATA

2

Inducing Structure into text-based data
There is a difference between structured and unstructured data. Text based data is
unstructured data. Structured data is sorted into categories and stored in fixed fields with a file or
a record. Structured data is commonly found in electronic resource planning (ERP) systems or
other organized data collection platforms (Aggarwal, & Zhai, 2012). On the other hand,
unstructured data lacks a predefined manner of arrangement. An example of unstructured data is
data collected from social media responses. Inducing structure into text-based data is the process
of classifying text data into fields with records or files which makes it easier to analyze. There
are several ways that text-data can be converted into structured data (Tu et al., 2016).
In order to structure text-data, an individual or organization can begin with the creation of
data dictionary which identifies some commonly repeated texts with important implications in
data analysis. These key words are words that the system finds to recur. After creating the
dictionary, the words that seems to recur and are not important to the analysis or are only
repeated few and insignificant times are discarded ...

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UCLA

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