Question 1

In the financial services firm case study, text analysis for associate-customer interactions were completely automated and could detect whether they met the company's standards.

Accepted Answer

The text analysis for associate-customer interactions in the financial services firm case study was completely automated and could detect whether they met the company's standards.

Question 2

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.

Accepted Answer

Sentiment analysis in voice of the customer applications can help companies monitor and understand customer feedback in real time, allowing them to make changes or adjustments to their products or services in response to customer sentiment. This can improve customer satisfaction and loyalty, ultimately contributing to better business outcomes.

Question 3

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

Accepted Answer

This is correct. Text mining involves analyzing unstructured textual data, such as those found in Word documents, PDF files, and email messages. Other formats, such as XML files and text excerpts, can also be analyzed through text mining techniques.

Question 4

Regional accents present challenges for natural language processing.

Accepted Answer

Regional accents can lead to variations in pronunciation, intonation, and word choice, making it difficult for natural language processing systems to accurately identify and understand spoken language.

Question 5

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

Accepted Answer

Categorization and clustering are two different text mining techniques. Categorization involves assigning documents to predefined categories, while clustering involves grouping documents into clusters based on their similarity. The preselection of categories is only relevant to categorization, not clustering.

Question 6

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

Accepted Answer

Sentiment, in the context of sentiment analysis, refers to a more stable, enduring attitude or opinion towards a particular subject or topic, rather than a transient, temporary feeling.

Question 7

In the patent analysis case study, text mining of thousands of patents held by the firm and its competitors helped improve competitive intelligence, but was of little use in identifying complementary products.

Accepted Answer

Text mining of patents can be very useful in identifying complementary products by analyzing the technology and application fields of patents, which can reveal potential areas for collaboration or complementary product development.

Question 8

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

Accepted Answer

The answer of The bag-of-words model is appropriate for spam...

Question 9

In the chapter's opening vignette, IBM's computer named Watson outperformed human game champions on the game show Jeopardy!

Accepted Answer

The opening vignette in the chapter describes how IBM's computer named Watson outperformed human game champions on the game show Jeopardy!.

Question 10

In the Hong Kong government case study, reporting time was the main benefit of using SAS Business Analytics to generate reports.

Accepted Answer

In the Hong Kong government case study, it was mentioned that SAS Business Analytics helped in reducing the reporting time significantly, making it the main benefit of using this tool.

Question 11

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.

Accepted Answer

This statement correctly describes the meaning of support in association rule mining. It represents the percentage of transactions (i.e., documents in text mining) in which both items (i.e., concepts in text mining) appear together.

Question 12

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.

Accepted Answer

This statement is true. Movie reviews often have a clear positive or negative sentiment, while news articles may contain more nuanced or factual content that is difficult to classify as solely positive or negative.

Question 13

In the BBVA case study, text analytics was used to help the company defend and enhance its reputation in social media.

Accepted Answer

The BBVA case study highlights the use of text analytics to monitor social media and respond to negative comments in order to protect and improve the company's reputation.

Question 14

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

Accepted Answer

The linguistic approach to speech focuses on the structure, use, and understanding of language, rather than the physical properties of sound such as intensity, pitch, and jitter, which are handled by the acoustic approach to speech analysis.

Question 15

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.

Accepted Answer

The answer of In text mining, creating the term-document matrix...

Question 16

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

Accepted Answer

Text mining is actually the broader term that encompasses text analytics. Text analytics involves analyzing text to derive insights and patterns, whereas text mining includes not only analytics but also the process of extracting high-quality information from text.

Question 17

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

Accepted Answer

This statement is true. Articles (e.g. "the", "a") and auxiliary verbs (e.g. "is", "am", "have") are considered stop words in text mining, meaning they do not have significant impact on the meaning of the text and are usually removed in pre-processing steps to improve analysis accuracy and efficiency.

Question 18

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

Accepted Answer

Chinese, Japanese, and Thai have complex writing systems, varying word order, and subtle nuances in grammar and vocabulary that make them more challenging for natural language processing algorithms. Additionally, Chinese and Japanese rely heavily on the use of ideograms, which do not correspond to a phonetic alphabet and can be difficult to parse without context.

Question 19

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

Accepted Answer

Current systems can achieve higher than 50% accuracy in detecting lies from text, though they are not yet perfect and still face challenges in accurately interpreting context and nuances.

Question 20

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

Accepted Answer

Entity recognition takes place before relationship extraction as it is necessary to identify the entities involved in order to extract their relationships.

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