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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 2 of 4

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Q11.
What is the primary goal of big sentiment data tracking?

a.

Analyzing the sentiment of individual documents

b.

Aggregating emotional information related to a specific topic over time

c.

Detecting and tracking events from text archives

d.

Identifying temporal relations between events

Discuss
Answer: (b).Aggregating emotional information related to a specific topic over time Explanation:The primary goal of big sentiment data tracking is to aggregate emotional information related to a specific topic over time and present it in an at-a-glance manner.
Q12.
What is the importance of identifying temporal relations between events in sentiment analysis?

a.

It is essential for document summarization

b.

It helps in answering temporal questions

c.

It is crucial for current information retrieval systems

d.

It aids in detecting events from text archives

Discuss
Answer: (b).It helps in answering temporal questions Explanation:Identifying temporal relations between events is important in sentiment analysis as it helps in answering temporal questions and is also crucial in other NLP applications.
Q13.
What is the primary benefit of Cambria et al.'s scalable methodology for fusing multiple cognitive and affective recognition modules in real time?

a.

It focuses on unimodal recognition modules only.

b.

It deals with temporal issues using multidimensional vector spaces.

c.

It relies on keyword spotting for sentiment analysis.

d.

It uses well-known measures for evaluating performance.

Discuss
Answer: (b).It deals with temporal issues using multidimensional vector spaces. Explanation:The methodology deals with temporal issues using multidimensional vector spaces, allowing it to fuse multiple recognition modules with different time scales.
Q14.
Which technique is used to control the ensemble stream in real time in Cambria et al.'s methodology?

a.

Cross-validation

b.

Percentage of correctly classified instances

c.

Kalman filtering

d.

Dimensional analysis

Discuss
Answer: (c).Kalman filtering Explanation:Kalman filtering is used to control the ensemble stream in real time and ensure temporal consistency and robustness in Cambria et al.'s methodology.
Q15.
What approach did Godbole et al. use for large-scale sentiment analysis of news and blogs?

a.

Syntactical analysis

b.

Domain adaptation

c.

Lexical affinity

d.

Statistical methods

Discuss
Answer: (a).Syntactical analysis Explanation:Godbole et al. used syntactical approaches for large-scale sentiment analysis of news and blogs.
Q16.
What were the seven sentiment dimensions chosen by Godbole et al. for sentiment analysis in their system?

a.

Happiness, sadness, anger, surprise, fear, trust, disgust

b.

General, health, crime, sports, business, politics, media

c.

Positive, neutral, negative, love, hate, indifference, excitement

d.

Joy, surprise, anger, anticipation, trust, fear, sadness

Discuss
Answer: (b).General, health, crime, sports, business, politics, media Explanation:Godbole et al. chose seven sentiment dimensions for sentiment analysis: general, health, crime, sports, business, politics, and media.
Q17.
What is one of the limitations of standard approaches to opinion mining?

a.

They rely on keyword spotting.

b.

They work well with implicit emotions.

c.

They can handle unstructured and fuzzy text data.

d.

They cannot correctly mine opinions when emotions are conveyed implicitly.

Discuss
Answer: (d).They cannot correctly mine opinions when emotions are conveyed implicitly. Explanation:One of the limitations of standard approaches to opinion mining is that they cannot correctly mine opinions when emotions are conveyed implicitly in the text.
Q18.
Why is affect detection considered critical in the context of affect-sensitive interfaces?

a.

To measure users' physical states

b.

To provide common-sense reasoning

c.

To enable affective categorization

d.

To respond to users' affective states

Discuss
Answer: (d).To respond to users' affective states Explanation:Affect detection is critical in affect-sensitive interfaces because such interfaces need to respond to users' affective states.
Q19.
What is a key challenge in affect detection?

a.

Fuzzy boundaries of emotions

b.

Perfect measurement of emotions

c.

Lack of individual differences in emotions

d.

Limited expression and experience of emotions

Discuss
Answer: (a).Fuzzy boundaries of emotions Explanation:A key challenge in affect detection is the fuzzy boundaries of emotions and substantial individual difference variations in expression and experience.
Q20.
How does the opinion-mining engine in sentic computing classify affective information?

a.

Only in a dimensional format

b.

Only in a categorical way

c.

Both in a categorical way and in a dimensional format

d.

Neither in a categorical way nor in a dimensional format

Discuss
Answer: (c).Both in a categorical way and in a dimensional format Explanation:The opinion-mining engine in sentic computing classifies affective information both in a categorical way (according to a wider number of emotion categories) and in a dimensional format.
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