Forecasting And Predictive Analytics With ForecastX 7th Edition By Holton Wilson – Test Bank

Chapter 11 Text Mining

1) Of all the data available today it is estimated that about 90% of data is
A) unstructured.
B) structured.
C) numerical.
D) graphical.

Answer: A
Difficulty: 1 Easy
Topic: Where to Start – The Bag of Words Analysis?
Learning Objective: 11-01 Explain the concept of “dimension reduction”.
Accessibility: Keyboard Navigation
Gradable: automatic

2) A major part of text mining is to
A) reduce the dimensions of the data.
B) generalize the use of modifiers.
C) screen the articles from the data set.
D) reduce the word count of the text actually used.

Answer: A
Difficulty: 1 Easy
Topic: Introduction
Learning Objective: 11-01 Explain the concept of “dimension reduction”.
Accessibility: Keyboard Navigation
Gradable: automatic

3) Semantic Processing seeks to
A) extract meaning.
B) group individual terms into bins.
C) eliminate “extra” or unnecessary terms from an analysis.
D) uncover undefined words or terms in a set of textual data.

Answer: A
Difficulty: 1 Easy
Topic: Back to the Usenet Example
Learning Objective: 11-02 Construct and employ two different dimension reduction algorithm types.
Accessibility: Keyboard Navigation
Gradable: automatic

4) In text mining, “knowledge discovery” refers to
A) extraction of codified features.
B) analysis of feature distribution.
C) counting the number of unknown terms used in a document.
D) the measurement of single-use terms present in the text.

Answer: A
Difficulty: 1 Easy
Topic: Newsgroup
Learning Objective: 11-05 Interpret the standard diagnostic statistics for text mining algorithms.
Accessibility: Keyboard Navigation
Gradable: automatic

5) “Information distillation” as used in text mining refers to
A) the analysis of the feature distribution.
B) reducing the number of words or phrases that need to be combed.
C) reducing a text by eliminating punctuation and word spacing.
D) measuring the strength of a model’s ability to predict.

Answer: A
Accessibility: Keyboard Navigation
Gradable: automatic

In the text mining example shown here, which part of the diagram represents the product of a text mining operation?
A) The node labeled “Sentiment_Analysis.”
B) The node labeled “Merge.”
C) The node labeled “CHAID Customer…”
D) The node labeled “Satisfaction_Survey.”

Answer: A
Difficulty: 1 Easy
Topic: Why Turn Texts into Numbers
Learning Objective: 11-02 Construct and employ two different dimension reduction algorithm types.
Accessibility: Keyboard Navigation
Gradable: automatic

7) As compared to data mining, text mining seeks to
A) extract features.
B) increase dimensionality of the data.
C) use “fuzzy” attributes in its algorithms.
D) reduce the speed of passes through the data.

Answer: A
Difficulty: 1 Easy
Topic: Where to Start – The Bag of Words Analysis?
Learning Objective: 11-01 Explain the concept of “dimension reduction”.
Accessibility: Keyboard Navigation
Gradable: automatic

Consider the IBM/SPSS Modeler stream shown here. The “Nugget” labeled “sentiment_analysis”
A) was constructed using a “merge model.”
B) was created by using the “Text Mining Node.”
C) contains the documents used to determine a sentiment analysis model.
D) is a placeholder for a text mining model to be later inserted in the stream.

Answer: B
Difficulty: 1 Easy
Topic: Natural Language Processing
Learning Objective: 11-04 Explain and use a “natural language processing” algorithm.
Accessibility: Keyboard Navigation
Gradable: automatic

9) _______ refers to the process of deriving high-quality information from text.
A) Text Mining
B) Image Mining
C) Database Mining
D) Multimedia Mining

Answer: A
Difficulty: 1 Easy
Topic: Where to Start – The Bag of Words Analysis?
Learning Objective: 11-01 Explain the concept of “dimension reduction”.
Accessibility: Keyboard Navigation
Gradable: automatic

10- Refer to the IBM/SPSS Modeler Stream here. What is the Node labeled “Seth Grodin Blog”?
A) The Node contains postings of a particular web blog.
B) The Node can either read an Excel file, a text file, or a “data” file.
C) The Node contains the rule set for interpreting text data; it is a Natural Language Processor.
D) The Node is a web feed node that collects data from a web URL.

Answer: D
Difficulty: 1 Easy
Topic: Natural Language Processing
Learning Objective: 11-04 Explain and use a “natural language processing” algorithm.
Accessibility: Keyboard Navigation
Gradable: automatic