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Welcome to the Big Social Data Analysis MCQs Page

Dive deep into the fascinating world of Big Social Data Analysis with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Big Social Data Analysis, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Big Social Data Analysis, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Big Data Computing.

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Check out the MCQs below to embark on an enriching journey through Big Social Data Analysis. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Big Data Computing.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Big Social Data Analysis. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Big Social Data Analysis MCQs | Page 4 of 4

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Q31.
How are concepts categorized in the AffectiveSpace module?

a.

By their alphabetical order

b.

By their position in the vector space representation

c.

By their relative frequency

d.

By their synonyms and antonyms

Discuss
Answer: (b).By their position in the vector space representation Explanation:Concepts are categorized in the AffectiveSpace module based on their position in the vector space representation of affective common-sense knowledge.
Q32.
What is the affective information used for in the sentic extraction process?

a.

To calculate semantic relatedness

b.

To determine the frequency of concepts

c.

To calculate a polarity value

d.

To detect synonyms and antonyms

Discuss
Answer: (c).To calculate a polarity value Explanation:The affective information is used to calculate a polarity value associated with each SBoC provided by the sentic parser.
Q33.
How does the engine ensure fast real-time opinion mining?

a.

By applying spectral association in real-time

b.

By directly applying the AffectiveSpace process

c.

By calculating sentic vectors at runtime

d.

By using SQL databases for precomputed resources

Discuss
Answer: (d).By using SQL databases for precomputed resources Explanation:The engine ensures fast real-time opinion mining by using SQL databases for precomputed resources, including sentic vectors and semantic classifications.
Q34.
What is the approximate processing time for the extraction of semantics and sentics when using SQL databases?

a.

In the order of milliseconds

b.

In the order of seconds

c.

In the order of minutes

d.

In the order of hours

Discuss
Answer: (b).In the order of seconds Explanation:The extraction of semantics and sentics is in the order of seconds when using SQL databases, which makes it faster compared to directly applying the AffectiveSpace and IsaCore processes.
Q35.
What is the primary challenge posed by the growth of user-generated content on the Web 2.0?

a.

Increasing complexity of online social data

b.

Decreasing user participation

c.

Lack of multimedia content

d.

Lack of social network analysis tools

Discuss
Answer: (a).Increasing complexity of online social data Explanation:The primary challenge posed by the growth of user-generated content on the Web 2.0 is the increasing complexity of online social data.
Q36.
What is the multidisciplinary approach recommended for analyzing big social data?

a.

Focusing solely on social media analytics

b.

Using only trend discovery techniques

c.

Combining different perspectives such as social media analytics, trend discovery, multimedia management, and more

d.

Relying exclusively on opinion mining

Discuss
Answer: (c).Combining different perspectives such as social media analytics, trend discovery, multimedia management, and more Explanation:The recommended approach for analyzing big social data is to combine different perspectives, including social media analytics, trend discovery, multimedia management, and more.
Q37.
What is the ultimate goal of a concept-level analysis of online social data?

a.

To reduce the size of social data

b.

To simplify the data for machine processing

c.

To aggregate semantics and sentics associated with text

d.

To eliminate user-generated content

Discuss
Answer: (c).To aggregate semantics and sentics associated with text Explanation:The ultimate goal of a concept-level analysis of online social data is to enable the aggregation of the semantics and sentics associated with text.
Q38.
What do next-generation opinion mining systems require to better understand natural language opinions?

a.

Smaller affective knowledge bases

b.

Less psychological reasoning

c.

Broader and deeper affective commonsense knowledge bases

d.

Less machine-processable data

Discuss
Answer: (c).Broader and deeper affective commonsense knowledge bases Explanation:Next-generation opinion mining systems require broader and deeper affective commonsense knowledge bases to better understand natural language opinions.
Q39.
What does the multidisciplinary approach aim to bridge more efficiently?

a.

The gap between structured and unstructured data

b.

The gap between user-generated and curated content

c.

The gap between human intelligence and machine intelligence

d.

The gap between social media analytics and trend discovery

Discuss
Answer: (a).The gap between structured and unstructured data Explanation:The multidisciplinary approach aims to bridge more efficiently the gap between (unstructured) human-processable information and (structured) machine-processable data.
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