Deck 10: Natural Language Processing - Text Mining and Sentiment Analysis

Question 1

According to the text, chatbots at ________ manage about 30 percent of the incoming volume, whereas the chatbots at ________ can manage almost 70 percent of customers' questions.

A) Domino's; Hertz
B) Brand 24; Marriott
C) Dish Network; Avis
D) Hidden Valley; Clorox

Answer 1

Dish Network; Avis

Question 2

Identify a true statement about unstructured data.

A) It requires further processing before it can be used for statistical methods.
B) It is made up of records that are organized in rows and columns.
C) This type of data can readily be stored in a database or spreadsheet format.
D) It is made up of predefined numbers, dates, and text strings.

Answer 2

It requires further processing before it can be used for statistical methods.

Question 3

In the preprocessing step of text analytics, ________ is the process of removing prefixes or suffixes from words to reduce them to their root form.

A) stop words removal
B) bag of words
C) stemming
D) lemmatization

Answer 3

stemming

Question 4

Which of the following is true of structured data?

A) It includes text, images, videos, and sensor data.
B) It represents more than 75 percent of emerging data.
C) It does not have a predefined arrangement.
D) It can be stored in a database or spreadsheet format.

Question 5

List and provide a description of the four steps of text analytics.

Question 6

Identify and describe the steps of the Latent Dirichlet Allocation algorithm of text modeling.

Question 7

Which of the following is the first step of text analytics?

A) text exploration
B) text acquisition and aggregation
C) text modeling
D) text preprocessing

Question 8

Describe how companies such as Domino's Pizza team up with virtual assistants that use voice recognition software to facilitate customer ordering.

Question 9

The Hidden Valley company uses natural language processing to identify and seize the opportunity to develop new products.

Question 10

For a specialty coffee company named Starcafe analyzing its online reviews, it is reasonable to categorize the words "Starcafe" and "coffee" as stop words.

Question 11

Describe how a frequency bar chart is used within the text exploration step of text analytics.

Question 12

Term frequency measures the frequency of a term (or word) over all documents, whereas inverse document frequency (IDF) measures the number of times a term (or word) occurs in a document.

Question 13

Which of the following is an example of sentiment opposite polarity?

A) "It rained all day today."
B) "My food arrived cold, and when I complained the server was impolite."
C) "Great coffee, great price, and friendly staff! Enjoyed the experience!"
D) "I woke up in a great mood today, but the hour-long commute ruined it."

Question 14

Some customer segments do not adapt well to voice-guided technology-they instead prefer traditional person-to-person communications.

Question 15

A frequency bar chart technique counts the occurrence of words in a document while ignoring the order or the grammar of words.

Question 16

Explain the N-gram technique of text analytics and include an example.

Question 17

The Clorox company has applied natural language processing to analyze social media conversations about cold and flu symptoms during the COVID-19 pandemic to optimize inventory in stores.

Question 18

Which of the following is a difference between structured and unstructured data?

A) Structured data exists in predefined formats, whereas unstructured data needs to be converted before usage.
B) Structured data requires further processing before analysis, whereas unstructured data can be readily processed.
C) Structured data consists of images and videos, whereas unstructured data consists of numbers, locations and dates.
D) Structured data does not readily fit into a tabular format, whereas unstructured data is characterized by rows and columns.

Question 19

Explain natural language processing and how marketers can use it to their advantage.

Question 20

How does aspect-based sentiment analysis help companies capture a granular understanding of customer preferences?

Question 21

In which of the four steps of text analytics is a corpus of text data defined?

A) text acquisition and aggregation
B) text modeling
C) text preprocessing
D) text exploration

Question 22

Explain how a marketer would use text classification to rate customer satisfaction.

Question 23

Describe how companies such as Avis and Dish Network use chatbots to enhance customer service.

Question 24

Summarize practitioner Jasmine Jones's views about the potential for natural language processing in marketing.

Question 25

Structured data represents more than 75 percent of the data emerging from the internet and social media.

Question 26

In the preprocessing step of text analytics, the words "best" and "writing" were reduced to "good" and "write." This is an example of ________.

A) lemmatization
B) stop words removal
C) N-grams
D) stemming

Question 27

"The store looked fabulous, but not a single shirt fit me well." This sentence is an example of sentiment polarity.

Question 28

A text analytics computer program separated the phrase "I like cake" into three sections: "I," "like," and "cake." This is an example of ________ within the text preprocessing step.

A) tokenization
B) stemming
C) lemmatization
D) stop words removal

Question 29

Explain how a term-document matrix helps in identifying a bag of words to preprocess text.

Question 30

As soon as Paula types in "The best coffee" in her search engine query box, the words "near me" and "recipe" appear as suggestions. The search engine uses the ________ technique to make such real-time recommendations possible.

A) term-document matrix
B) N-gram
C) frequency bar chart
D) bag of words

Question 31

Explain the difference in how the results are presented between a word cloud based on the Porter stemming algorithm and a word cloud based on the lemmatization algorithm.

Question 32

Explain the lemmatization technique of text analytics and provide several examples.

Question 33

Which of the following techniques or algorithms is used in search engines to recommend the next character or word in real time when a user is typing a search query?

A) frequency bar chart
B) N-grams
C) term-document matrix
D) bag of words

Question 34

In text preprocessing, the term-document matrix consists of the x-axis that represents terms and the y-axis that represents the frequency of a particular term occurring.

Question 35

As per the text, which company applies natural language processing to make discoveries in social media conversations, so that it can make the best decisions about adding features such as seat heaters to its products?

A) Ford
B) Avis
C) Subaru
D) Uber

Question 36

Identify an example of sentiment opposite polarity.

A) "I could not buy the items I wanted as the shelves were empty."
B) "My pizza was delicious, and I loved the ambience of the restaurant."
C) "I enjoyed my coffee, but the barista was rude."
D) "I jogged for an hour today morning, and I feel awesome."

Question 37

Describe how the Royal Bank of Scotland uses text data from various sources to strengthen its customer relationships.

Question 38

According to the text, which of the following companies assesses user experiences using Brand 24, a social media monitoring app, to make additional improvements?

A) Clorox
B) Uber
C) Marriott
D) Lyft