Page 97 - B.Tech IT Curriculum and Syllabus R2017 - REC

Department of IT, REC

Influence - Influence Maximization in Viral Marketing - Algorithms and Systems for Expert Location
in Social Networks - Expert Location without Graph Constraints - with Score Propagation – Expert
Team Formation - Link Prediction in Social Networks - Feature based Link Prediction - Bayesian
Probabilistic Models - Probabilistic Relational Models.

UNIT V TEXT AND OPINION MINING 9
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining –Opinion Spam Detection- Wish
analysis - Product review mining – Review Classification – Tracking sentiments towards topics over
time.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, the student should be able to:
1. Work on the internals components of the social network.
2. Model and visualize the social network.
3. Mine the behaviour of the users in the social network.
4. Predict the possible next outcome of the social network.
5. Mine the opinion of the user.

TEXT BOOKS:
1. Peter Mika, Social Networks and the Semantic Web, Springer, First edition, 2007.
2. BorkoFurht, Handbook of Social Network Technologies and Applications, Springer, First
edition, 2010.

REFERENCES:
1. Charu C. Aggarwal, Social Network Data Analytics, Springer; 2011
2. GuandongXu , Yanchun Zhang and Lin Li, Web Mining and Social Networking – Techniques
and applications, Springer, First edition, 2011.
3. Giles, Mark Smith, John Yen, Advances in Social Network Mining and Analysis, Springer,
2010.
4. Ajith Abraham, Aboul Ella Hassanien, VaclavSnašel, Computational Social Network
Analysis: Trends, Tools and Research Advances, Springer, 2009.
5. Toby Segaran, Programming Collective Intelligence, O‘Reilly, 2012
6. Bing Liu. Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers, May
2012.

SEMESTER VIII
ELECTIVE - V

Curriculum and Syllabus | B.Tech. Information Technology | R2017 Page 97