DSS > chapter 7 part2 > Flashcards

chapter 7 part2 Flashcards

(78 cards)

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1
Q

What does the term sentiment refer to according to the slide?

A

Belief view opinion and conviction

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2
Q

Sentiment analysis tries to answer what main question?

A

What do people feel about a certain topic

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3
Q

How does sentiment analysis answer what people feel about a topic?

A

By analyzing data related to opinions of many using a variety of automated tools

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4
Q

Sentiment analysis is used in many domains but is especially noteworthy in which area?

A

CRM related to customers and consumers opinions

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5
Q

What does VOC stand for in sentiment analysis applications?

A

Voice of the customer

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6
Q

What does VOM stand for in sentiment analysis applications?

A

Voice of the market

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7
Q

What does VOE stand for in sentiment analysis applications?

A

Voice of the employee

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8
Q

List one sentiment analysis application related to branding.

A

Brand management

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9
Q

List one sentiment analysis application related to finance.

A

Financial markets

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10
Q

List one sentiment analysis application related to politics.

A

Politics

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11
Q

List one sentiment analysis application related to government.

A

Government intelligence

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12
Q

List one sentiment analysis application related to online business.

A

E commerce site design

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13
Q

List one sentiment analysis application related to communication.

A

Email filtering

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14
Q

In the sentiment analysis process diagram what is the initial input?

A

Textual data

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15
Q

In Step 1 of the diagram what is calculated?

A

O S polarity

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16
Q

What resource is used in Step 1 according to the diagram?

A

A lexicon

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17
Q

What question is asked after calculating O S polarity?

A

Is there a sentiment

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18
Q

If sentiment exists what is produced from Step 1?

A

O S polarity measure

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19
Q

In Step 2 of the diagram what polarity is calculated?

A

N P polarity

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20
Q

In Step 3 of the diagram what is identified?

A

The target for the sentiment

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21
Q

What is Step 4 in the diagram?

A

Tabulate and aggregate the sentiment analysis results

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22
Q

Step 1 of the sentiment analysis process is called what?

A

Sentiment detection

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23
Q

Step 1 of sentiment analysis process
comes right after what activities?

A

Retrieval and preparation of the text documents

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24
Q

Step 1 of the sentiment analysis process (Sentiment Detection) is also called detection of what?

A

Objectivity

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25
Fact corresponds to what in Step 1?
Objectivity
26
Opinion corresponds to what in Step 1?
Subjectivity
27
Step 2 of the Sentiment Analysis process is called what?
N P polarity classification
28
What are the two opposing sentiment polarities in Step 2?
Negative and positive
29
Step 3 of the sentiment analysis process is called what?
Target identification
30
What is the goal of target identification?
To accurately identify the target of the expressed sentiment
31
List examples of sentiment targets mentioned.
A person a product an event
32
What determines the difficulty of target identification?
The application domain
33
Step 4 of the Sentiment Analysis process is called what?
Collection and aggregation
34
Across which levels are sentiments aggregated?
Word --> Statement --> Paragraph --> Document
35
One method of polarity identification uses what as a reference library?
A lexicon dictionary
36
How can lexicons be developed?
Manually or automatically
37
Name one lexicon listed.
WordNet
38
Name another lexicon listed.
SentiWordNet
39
Name another lexicon listed.
WordNet Affect
40
Another method of polarity identification uses what source of knowledge?
A collection of training documents
41
What is induced from opinionated textual documents?
Predictive models
42
Polarity is learned within what context using training documents?
A specific domain
43
According to the slide what is the largest repository of data?
The Web
44
Web data exists in which formats?
HTML XML and text
45
One challenge of Web mining is that the Web is too what?
Big
46
Another challenge of Web mining is that the Web is too what?
Complex
47
Another challenge of Web mining is that the Web is too what?
Dynamic
48
Another challenge is that the Web is not what?
Specific to a domain
49
Another challenge is that the Web has what?
Everything
50
What is web content mining?
Extraction of useful information from Web pages unstructured textual content
51
Web content mining data is collected via what?
Web crawlers
52
Competitive intelligence in web content mining involves collecting what?
Competitors products services and customers
53
Web content mining can be used for collecting what types of content?
* Gather information about competitors (their products, services, customers) * Collect news, information, and people’s opinions * Improve search engine results * Create summaries of large amounts of text * Analyze emotions and opinions (sentiment analysis) * Automatically collect and organize data for predictions and analysis
54
Web content mining can enhance the results produced by what?
Search engines
55
Web content mining can be used for what document task?
Summarization
56
Web content mining can support which analysis technique?
Sentiment analysis
57
Web content mining supports automated data collection for what?
Predictive modeling
58
What do Web pages include according to the slide?
Hyperlinks
59
Hyperlinks contain what?
Hidden human annotation
60
What are authoritative pages?
Pages collectively endorsed indicating importance
61
What are hubs?
Pages that provide a collection of links to authoritative pages
62
What does HITS stand for?
Hyperlink induced topic search
63
What does the HITS algorithm calculate?
Hubs and authorities
64
Web usage mining is also called what?
Web analytics
65
Web usage mining extracts information from what?
Data generated through Web page visits and transactions
66
What is clickstream data?
Data stored in server access logs referrer logs and client side cookies
67
Clickstream data includes what about users?
User characteristics and usage profiles
68
Clickstream data includes what metadata?
Page attributes content attributes usage data time of access and duration
69
What is clickstream analysis?
Analysis of clickstream data using data mining and text mining techniques
70
One application of web usage mining is determining what?
Lifetime value of clients
71
Web usage mining can be used to design what?
Cross marketing strategies across products
72
Web usage mining can evaluate what?
Promotional campaigns
73
Web usage mining can target what?
Electronic ads and coupons
74
Targeting is based on what?
User access patterns
75
Web usage mining can predict what?
User behavior
76
User behavior prediction is based on what?
Previously learned rules and users profiles
77
Web usage mining can present what?
Dynamic information
78
Dynamic information is presented based on what?
Users interests and profiles
DSS
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