The Database Quiz

1. Regional accents present challenges for natural language processing

True

False

2. In text mining, inputs to the process include unstructured data such as Word documents, PDF files, text excerpts, e-mail and XML files

True

False

3. Current use of sentiment analysis in voice of the customer applications allows companies to change their products or services in real time in response to customer sentiment.

True

False

4. In sentiment analysis, it is hard to classify some subjects such as news as good or bad, but easier to classify others, e.g., movie reviews, in the same way.

True

False

5. In text mining, if an association between two concepts has 7% support, it means that 7% of the documents had both concepts represented in the same document

True

False

6. Chinese, Japanese, and Thai have features that make them more difficult candidates for natural language processing

True

False

7. Articles and auxiliary verbs are assigned little value in text mining and are usually filtered out

True

False

8. The bag-of-words model is appropriate for spam detection but not for text analytics.

True

False

9. According to a study by Merrill Lynch and Gartner, what percentage of all corporate data is captured and stored in some sort of unstructured form

15%

85%

25%

75%

10. . In text analysis, what is a lexicon?

A catalog of words, their synonyms, and their meanings

A catalog of customers, their words, and phrase

A catalog of customers, products, words, and phrase

A catalog of letters, words, phrases and sentences

11. . In text mining, stemming is the process of

Creating new branches or stems of recorded paragraphs.

) reducing multiple words to their base or root

Categorizing a block of text in a sentence.

Transforming the term-by-document matrix to a manageable size.

12. Categorization and clustering of documents during text mining differ only in the preselection of categories

True

False

13. . What application is MOST dependent on text analysis of transcribed sales call center notes and voice conversations with customers?

CRM

OLAP

Finance

ERP

14. What data discovery process, whereby objects are categorized into predetermined groups, is used in text mining?

Classification

Trend analysis

Association

Clustering

15. All of the following are challenges associated with natural language processing EXCEPT

Dividing up a text into individual words in English.

Understanding the context in which something is said

Recognizing typographical or grammatical errors in texts.

Distinguishing between words that have more than one meaning

16. . In sentiment analysis, which of the following is an implicit opinion?

The cruise we went on last summer was a disaster

Our new mayor is great for the city.

The customer service I got for my TV was laughable.

The hotel we stayed in was terrible.

17. 4. During information extraction, entity recognition (the recognition of names of people and organizations) takes place after relationship extraction

True

False

18. . In text mining, tokenizing is the process of

Transforming the term-by-document matrix to a manageable size

Reducing multiple words to their base or roo

Categorizing a block of text in a sentence

Creating new branches or stems of recorded paragraphs

19. Inputs to speech analytics include all of the following EXCEPT

Written transcripts of calls to service centers

Recorded conversations of customer call-ins.

Videos of customer focus groups.

Live customer interactions with service representatives

20. Which of these applications will derive the LEAST benefit from text mining

Sales transaction file

Patent description files

Customer comment files

Patients' medical files

21. The linguistic approach to speech handles processes elements such as intensity, pitch and jitter from speech recorded on audio

True

False

22. . How is objectivity handled in sentiment analysis?

It is clarified with the customer who expressed it

It is incorporated as a type of sentiment.

It is identified and removed as facts are not sentiment

It is ignored because it does not appear in customer sentiment

23. Text analytics is the subset of text mining that handles information retrieval and extraction, plus data mining

True

False

24. . In text mining, creating the term-document matrix includes all the terms that are included in all documents, making for huge matrices only manageable on computers

True

False

25. Sentiment classification usually covers all the following issues EXCEPT

Range of polarity (e.g., star ratings for hotels and for restaurants).

Classes of sentiment (e.g., positive versus negative).

Range in strength of opinion

Biometric identification of the consumer expressing the sentiment.

26. What do voice of the market (VOM) applications of sentiment analysis do?

They examine the "market of ideas" in politics

They examine employee sentiment in the organization.

They examine customer sentiment at the aggregate level.

They examine the stock market for trends.

27. In sentiment analysis, sentiment suggests a transient, temporary opinion reflective of one's feelings

True

False

28. Identifying the target of an expressed sentiment is difficult for all the following reasons EXCEPT

The review may not be directly connected to the target through the topic name.

Sometimes there are multiple targets expressed in a sentiment.

Strong sentiments may be generated by a computer, not a person.

Blogs and articles with the sentiment may be general in nature.

29. Detecting lies from text transcripts of conversations is a future goal of text mining as current systems achieve only 50% accuracy of detection.

True

False

30. . In text mining, which of the following methods is NOT used to reduce the size of a sparse matrix?

Eliminating rarely occurring terms

Using singular value decomposition

Using a domain expert

Normalizing word frequencies

31. What types of documents are BEST suited to semantic labeling and aggregation to determine sentiment orientation?

Collections of documents

Medium- to large-sized documents

Large-sized documents

Small- to medium-sized documents