Question 1

What types of documents are BEST suited to semantic labeling and aggregation to determine sentiment orientation?
A) medium- to large-sized documents
B) small- to medium-sized documents
C) large-sized documents
D) collections of documents

Accepted Answer

Small- to medium-sized documents are best suited to semantic labeling and aggregation for determining sentiment orientation because they can be easily processed by algorithms and provide enough context to determine sentiment accurately. Large-sized documents and collections of documents may require more complex processing and may not provide clear sentiment orientation.

Question 2

What do voice of the market (VOM) applications of sentiment analysis do?
A) They examine customer sentiment at the aggregate level.
B) They examine employee sentiment in the organization.
C) They examine the stock market for trends.
D) They examine the "market of ideas" in politics.

Accepted Answer

Voice of the market (VOM) applications of sentiment analysis examine customer sentiment at the aggregate level, analyzing trends and patterns in customer feedback and social media conversations about a particular brand or product. They help companies understand the opinions and perceptions of their customers, and can be used to inform product development, marketing strategies, and customer service initiatives.

Question 3

All of the following are challenges associated with natural language processing EXCEPT
A) dividing up a text into individual words in English.
B) understanding the context in which something is said.
C) distinguishing between words that have more than one meaning.
D) recognizing typographical or grammatical errors in texts.

Accepted Answer

Dividing up a text into individual words in English (tokenization) is a relatively straightforward task in NLP, especially for languages like English that use clear spaces and punctuation marks to separate words. Challenges in NLP more commonly involve understanding context, handling words with multiple meanings, and recognizing errors in texts.

Question 4

In text analysis, what is a lexicon?
A) a catalog of words, their synonyms, and their meanings
B) a catalog of customers, their words, and phrase
C) a catalog of letters, words, phrases and sentences
D) a catalog of customers, products, words, and phrase

Accepted Answer

A lexicon is a catalog of words, their synonyms, and meanings. It is used in text analysis to identify and categorize words in text data.

Question 5

How is objectivity handled in sentiment analysis?
A) It is ignored because it does not appear in customer sentiment.
B) It is incorporated as a type of sentiment.
C) It is clarified with the customer who expressed it.
D) It is identified and removed as facts are not sentiment.

Accepted Answer

Objectivity is identified and removed as it pertains to factual information rather than sentiment. Sentiment analysis is concerned with identifying and analyzing opinions, attitudes, and emotions. Facts are important, but they are not considered sentiment. Therefore, they are removed from the analysis to maintain the integrity of the sentiment analysis model.

Question 6

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?
A) 15%
B) 75%
C) 25%
D) 85%

Accepted Answer

According to studies by Merrill Lynch and Gartner, 85% of all corporate data is captured and stored in some sort of unstructured form. This highlights the vast amount of data that exists outside of traditional, structured databases and the challenges and opportunities it presents for data management and analysis.

Question 7

In text mining, stemming is the process of
A) categorizing a block of text in a sentence.
B) reducing multiple words to their base or root.
C) transforming the term-by-document matrix to a manageable size.
D) creating new branches or stems of recorded paragraphs.

Accepted Answer

Stemming is the process of reducing a word to its base or root form, so that variations of a word can be treated as the same word. For example, the words "running," "run," and "runner" can be stemmed to the base word "run." This is commonly used in text mining and natural language processing.

Question 8

In sentiment analysis, which of the following is an implicit opinion?
A) The hotel we stayed in was terrible.
B) The customer service I got for my TV was laughable.
C) The cruise we went on last summer was a disaster.
D) Our new mayor is great for the city.

Accepted Answer

"Laughable" is an implicit opinion as it suggests that the customer did not receive satisfactory customer service. The other statements explicitly state the opinion, either positive or negative.

Question 9

In the Whirlpool case study, the company sought to better understand information coming from which source?
A) customer transaction data
B) delivery information
C) customer e-mails
D) goods moving through the internal supply chain

Accepted Answer

Whirlpool sought to better understand information coming from customer e-mails to improve their customer service and operational efficiency.

Question 10

In text mining, tokenizing is the process of
A) categorizing a block of text in a sentence.
B) reducing multiple words to their base or root.
C) transforming the term-by-document matrix to a manageable size.
D) creating new branches or stems of recorded paragraphs.

Accepted Answer

Tokenizing in text mining refers to breaking down a block of text into smaller units, such as words or sentences, for further analysis.

Question 11

Identifying the target of an expressed sentiment is difficult for all the following reasons EXCEPT
A) the review may not be directly connected to the target through the topic name.
B) blogs and articles with the sentiment may be general in nature.
C) strong sentiments may be generated by a computer, not a person.
D) sometimes there are multiple targets expressed in a sentiment.

Accepted Answer

The given options explain the reasons why identifying the target of an expressed sentiment may be difficult. However, option C is an incorrect statement as sentiments cannot be generated by a computer, but are expressed by a person.

Question 12

In the opening vignette, the architectural system that supported Watson used all the following elements EXCEPT
A) massive parallelism to enable simultaneous consideration of multiple hypotheses.
B) an underlying confidence subsystem that ranks and integrates answers.
C) a core engine that could operate seamlessly in another domain without changes.
D) integration of shallow and deep knowledge.

Accepted Answer

The opening vignette mentions the use of massive parallelism, an underlying confidence subsystem, and integration of shallow and deep knowledge in the architectural system that supported IBM Watson, but does not mention the ability to operate seamlessly in another domain without changes.

Question 13

Which of these applications will derive the LEAST benefit from text mining?
A) patients' medical files
B) patent description files
C) sales transaction files
D) customer comment files

Accepted Answer

Text mining can provide valuable insights for analyzing and improving complex data such as medical files, patent description files, and customer comment files. However, sales transaction files typically contain numerical data such as transaction dates, amounts, and products purchased which may not be as useful for text mining as compared to other data analysis methods such as regression analysis or data visualization.

Question 14

Inputs to speech analytics include all of the following EXCEPT
A) written transcripts of calls to service centers.
B) recorded conversations of customer call-ins.
C) live customer interactions with service representatives.
D) videos of customer focus groups.

Accepted Answer

The answer of Inputs to speech analytics include all of...

Question 15

What application is MOST dependent on text analysis of transcribed sales call center notes and voice conversations with customers?
A) finance
B) OLAP
C) CRM
D) ERP

Accepted Answer

CRM (Customer Relationship Management) is a system that manages interactions with customers and potential customers. Text analysis of transcribed sales call center notes and voice conversations with customers can provide insights into customer behavior, preferences, and emotions, which are crucial for improving customer experience and loyalty. Therefore, CRM is the application that is most dependent on text analysis of transcribed sales call center notes and voice conversations with customers.

Question 16

In the research literature case study, the researchers analyzing academic papers extracted information from which source?
A) the paper abstract
B) the paper keywords
C) the main body of the paper
D) the paper references

Accepted Answer

The answer of In the research literature case study, the...

Question 17

Sentiment classification usually covers all the following issues EXCEPT
A) classes of sentiment (e.g., positive versus negative).
B) range of polarity (e.g., star ratings for hotels and for restaurants).
C) range in strength of opinion.
D) biometric identification of the consumer expressing the sentiment.

Accepted Answer

Sentiment classification typically focuses on identifying and categorizing the sentiment expressed in text, such as determining whether it is positive or negative, and the strength of the sentiment. Biometric identification of the consumer expressing the sentiment is not typically part of sentiment classification.

Question 18

In text mining, which of the following methods is NOT used to reduce the size of a sparse matrix?
A) using a domain expert
B) normalizing word frequencies
C) using singular value decomposition
D) eliminating rarely occurring terms

Accepted Answer

The answer of In text mining, which of the following...

Question 19

What data discovery process, whereby objects are categorized into predetermined groups, is used in text mining?
A) clustering
B) association
C) classification
D) trend analysis

Accepted Answer

The data discovery process used in text mining that involves categorizing objects into pre-determined groups is called classification. Clustering involves grouping similar objects together based on their proximity, association involves finding relationships between different objects or concepts, and trend analysis involves identifying patterns and changes in data over time. Therefore, the best choice is C - classification.

Question 20

In the Blue Cross Blue Shield case study, speech analytics were used to identify "confusion" calls by customers. What was true about these calls?
A) They took less time than others as frustrated customers hung up.
B) They led customers to rely more on self-serve options.
C) They were not documented by customer service reps for speech analytics.
D) They were difficult to identify using standard phrases like "I don't get it."

Accepted Answer

The answer of In the Blue Cross Blue Shield case...

Quiz 7: Text Analytics, Text Mining, and Sentiment Analysis

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

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

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

In text analysis, what is a lexicon?

How is objectivity handled in sentiment analysis?

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?

In text mining, stemming is the process of

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

In the Whirlpool case study, the company sought to better understand information coming from which source?

In text mining, tokenizing is the process of

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

In the opening vignette, the architectural system that supported Watson used all the following elements EXCEPT

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

Inputs to speech analytics include all of the following EXCEPT

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

In the research literature case study, the researchers analyzing academic papers extracted information from which source?

Sentiment classification usually covers all the following issues EXCEPT

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

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

In the Blue Cross Blue Shield case study, speech analytics were used to identify "confusion" calls by customers. What was true about these calls?