Deck 7: Text Mining, Sentiment Analysis, and Social Analytics

Question 1

In the chapter's opening vignette, Amadori sought to better understand and market its food products effectively to customers of its food products.

Answer 1

True

Question 2

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

Answer 2

False

Question 3

Regional accents present challenges for natural language processing.

Answer 3

True

Question 4

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

Question 5

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.

Question 6

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.

Question 7

Natural language processing (NLP) is an important component of text mining and is a subfield of computational linguistics but not artificial intelligence.

Question 8

Because of the sheer size of the Web, it is not feasible to set up a data warehouse to replicate, store, and integrate all of the data on the Web.

Question 9

How would you describe information extraction in text mining?

Question 10

What is a Web crawler and what function does it serve in a search engine?

Question 11

What is search engine optimization (SEO) and why is it important for organizations that own Web sites?

Question 12

What is the difference between white hat and black hat SEO activities?

Question 13

How would you define clickstream analysis?