CO-PO Mapping 

            Lesson Plan                                                                                               CO-P0 Mapping Justification

Sample Question  Bank:

1. What is Social Network Analysis (SNA), and why is it important?

2. Define the following terms in SNA: node, edge, degree centrality.

3. What is the difference between directed and undirected networks?

4. Explain the concept of clustering coefficient in a network.

5. What are the different types of centrality measures in SNA?

6. How is betweenness centrality different from closeness centrality?

7. What is a hub and authority in social networks?

8. Explain the concept of community detection in SNA.

9. How does the Page Rank algorithm work in ranking nodes?

10. What are the common tools used for Social Network Analysis?

Sentiment Analysis

11. What is Sentiment Analysis, and how is it applied in real-world scenarios?

12. What are the main approaches to Sentiment Analysis?

13. Explain the difference between lexicon-based and machine learning-based sentiment analysis.

14. What is polarity in sentiment analysis?

15. How does a Bag of Words (BoW) model work in sentiment analysis?

16. What are some common challenges in Sentiment Analysis?

17. What is the role of Natural Language Processing (NLP) in Sentiment Analysis?

18. How do you handle sarcasm and irony in Sentiment Analysis?

19. What are some widely used datasets for Sentiment Analysis?

20. Name some Python libraries used for Sentiment Analysis.

 Q1. Mid Term Question Paper 2024-25 ( SEPT ) 

Practice Question Paper (From All Unit):

Subject: Social Network Analysis & Sentiment Analysis

Total Marks: 100 | Time: 3 Hours 

1. Define Social Network Analysis (SNA) and explain its significance.

2. Differentiate between social networks and random networks with examples.

3. What are the key components of a social network? Explain with a diagram.

4. What is network density, and how is it calculated?

5. Explain the concepts of homophily and heterophily in social networks.

6. Define and differentiate between:
   a) Degree Centrality and Betweenness Centrality
   b) Closeness Centrality and Eigenvector Centrality

7. Explain the PageRank algorithm and its applications.

8. What is a small-world network? Give a real-life example.

9. Define modularity and explain how it helps in community detection.

10. How do you detect and interpret weak ties in a social network?

11. What is a bipartite graph, and how is it used in social networks?

12. Explain the concept of triadic closure in networks.

13. Differentiate between directed and undirected graphs with examples.

14. Write a short note on Erdős–Rényi model and Barabási–Albert model.

15. How do adjacency matrix and incidence matrix represent network data?

16. Define Sentiment Analysis and list some of its real-world applications.

17. Explain the difference between lexicon-based and machine learning-based Sentiment Analysis.

18. What are the major challenges in sentiment classification?

19. Describe how a Bag of Words (BoW) model works in Sentiment Analysis.

20. Explain the role of Natural Language Processing (NLP) in Sentiment Analysis.

21. How does word embedding (Word2Vec, GloVe) improve sentiment analysis?

22. Explain how Long Short-Term Memory (LSTM) networks are used in Sentiment Analysis.

23. What is aspect-based sentiment analysis? Give an example.

24. How do you handle sarcasm detection in sentiment analysis?

25. What are some popular datasets used for training sentiment analysis models?

Mid Term Question Paper -1

Mid Term Question Paper- 2

End Term Question Paper  

Teaching Pedagogy