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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 3 of 4

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Q21.
What is the primary purpose of the preprocessing module in the opinion-mining engine?

a.

To extract concepts from opinionated text

b.

To spread negation in the text

c.

To convert text to lowercase

d.

To split opinions into single clauses

Discuss
Answer: (b).To spread negation in the text Explanation:The primary purpose of the preprocessing module is to detect negation and spread it in the text so that it can be associated with concepts during the parsing phase.
Q22.
Why is handling negation important in opinion- and sentiment-related analysis?

a.

Negation can enhance the meaning of a statement.

b.

Negation can create humor in text.

c.

Negation can reverse the meaning of a statement.

d.

Negation can increase the length of an opinion.

Discuss
Answer: (c).Negation can reverse the meaning of a statement. Explanation:Handling negation is important because it can reverse the meaning of a statement, which is crucial for sentiment analysis.
Q23.
What is the role of constructions in the parsing process of the sentic parser?

a.

To convert text to lowercase

b.

To identify meaningful stretches of text

c.

To determine which lexical items are matched

d.

To split opinions into single clauses

Discuss
Answer: (c).To determine which lexical items are matched Explanation:Constructions help the sentic parser determine which lexical items are matched by which constructions and how good each match is.
Q24.
How does the sentic parser efficiently choose the best construction for each span of text?

a.

By performing phrase structure analysis

b.

By comparing potential memberships of lexical items

c.

By using emoticons as guides

d.

By conducting sentiment analysis

Discuss
Answer: (b).By comparing potential memberships of lexical items Explanation:The sentic parser chooses the best construction for each span of text by comparing potential memberships of lexical items with the categories specified for each construction.
Q25.
What does the Sentic parser provide for each retrieved concept?

a.

Relative frequency

b.

Valence

c.

Status

d.

All of the above

Discuss
Answer: (d).All of the above Explanation:The Sentic parser provides, for each retrieved concept, its relative frequency, valence, and status.
Q26.
What is the purpose of the IsaCore and AffectiveSpace modules in analyzing the small bag of concepts (SBoC)?

a.

To detect synonyms and antonyms

b.

To infer conceptual and affective information

c.

To perform coarse sense disambiguation

d.

To calculate semantic relatedness

Discuss
Answer: (b).To infer conceptual and affective information Explanation:The IsaCore and AffectiveSpace modules are used to infer the conceptual and affective information associated with the input text from the SBoC.
Q27.
How is coarse sense disambiguation performed when multiple senses of a concept are detected?

a.

By averaging the vectors of all concepts in the clause

b.

By selecting the most common sense

c.

By using synonyms and antonyms

d.

By conducting a spectral analysis

Discuss
Answer: (a).By averaging the vectors of all concepts in the clause Explanation:Coarse sense disambiguation is performed by averaging the vectors of all concepts in the clause.
Q28.
What is the role of seeds in the IsaCore module?

a.

To represent the expected semantic value of the clause

b.

To detect short paths in the semantic network

c.

To spread activation to all concepts

d.

To classify concepts based on valence

Discuss
Answer: (a).To represent the expected semantic value of the clause Explanation:Seeds in the IsaCore module represent the expected semantic value of the clause as a whole.
Q29.
How is the classification measure of concepts in the SBoC determined?

a.

By their relative frequency

b.

By their valence

c.

By their degree of connectivity to seed concepts

d.

By their spectral association

Discuss
Answer: (c).By their degree of connectivity to seed concepts Explanation:The classification measure of concepts in the SBoC is determined by their degree of connectivity to seed concepts.
Q30.
What does the AffectiveSpace module do with the concepts from each SBoC?

a.

Assigns synonyms and antonyms to them

b.

Projects them into AffectiveSpace

c.

Calculates their relative frequency

d.

Stores them in an SQL database

Discuss
Answer: (b).Projects them into AffectiveSpace Explanation:The AffectiveSpace module projects the concepts from each SBoC into AffectiveSpace.
Page 3 of 4

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