CHAITANYA BHARATHI INSTITUTE OF TECHNOLOGY (A)
IT1-Social Media Analytics (Ele-3)-VI Sem-2020-2021
0%
Previous
Course data
General
Announcements
Please fill the Exit Survey Form
Marks Details SMA
MID I Marks SMA
MID II Marks SMA
Assignment Marks
Reference Texts Book to Download
Zafarani R., Abbasi M.A., Liu H, “Social Media Mining: An Introduction”, Cambridge University Press, 2014.
Lutz Finger, Soumitra Dutta, “Ask, Measure, Learn: Using Social Media Analytics to Understand and Influence Customer Behavior”, O’Reilly Media, 2014.
Drive link to download the above textbooks (if above links are not working)
Drive Link to Download Unit Vise PPTs
Social Media Analytics _18ITE09_ Unit Wise PPTs
Online Classes
U1-Introduction: Social Media Mining, New Challenges for Mining-08022021-1020
U1-Graph Essentials: Graph Basics, Graph Representation-10022021-0910
U1-Types of Graphs-12022021-1310
U1-Connectivity in Graphs, Special Graphs-12022021-1520
U1-Graph Algorithms -BFS, DFS, DijKstra, Ford fulkerson Algorithm, Bridge detection Algorithm-- 16022021-0910
U1-Centrality, Degree Centrality, Eigen vector centrality,-18022021-0910
U1- Katz Centrality, Pagerank centrality,-22022021-0910
U1-Page rank, Betweeness centrality,-23022021-0910
U1-Closeness, Group centrality-25022021-0910
U1-Balance status-01032021-0910
U1-Network Models-02032021-0910
U2- Community Analysis, Community Detection Algorithms, Member Based Community Detection
U2- Unit2 Questionare and Clique Percolation Method
U2 Node Similarity, Ratio, Normalized cut, Modularity, Normalized Modularity
U2 Modularity Maximization, Heirarchical Communities, Edge Betweeness, GirvanNewman Algorithm, Community Evolution and Evaluation
U2 Precision, Recall, F Measure, Mutual Information, Information Diffusion, Herd Behaviour, Herding All Examples
U2 Information Cascade, Independent Cascade Model, Maximizing the Spread of Cascades, Information Intervention, DIffusion of Innovation Model, Diffusion Case Studies
U2 DIffusion of Innovation Model, Diffusion Case Studies , Epidemics
U3 Data Mining Essentials: Data, Data Preprocessing
U3- Data Preprocessing: Supervised Learning, Classification
U3_ Data Preprocessing_ Supervised Learning_Regression_and_Unsupervised Learning
U3_Influence and Homophily_ Measuring Assortativity
U3_Measuring Homophily
U4_Recommedation in Social Media: Challenges, Classical Recommendation Algorithms Part 1
U4_Classical Recommendation Algorithms Part 2, Recommendation Using Social Context, Evaluating Recommendations
U4_Behavior Analytics: Individual Behavior
U4_Behavior Analytics: Collective Behavior
U5_Prediction_the_Future
U5_Prediction_of Learning_&_Predicting_Elections
U5_Prediction_of Box Offices
U5_Predicting_StockMarket_Closing Predictions
Material
U1-Introduction: Social Media Mining, New Challenges for Mining-08022021-1020
U1-Graph Essentials: Graph Basics, Graph Representation-10022021-0910
U1-Types of Graphs-12022021-1310
U1-Connectivity in Graphs, Special Graphs-12022021-1520
U1-Graph Algorithms -BFS, DFS, DijKstra, Ford fulkerson Algorithm, Bridge detection Algorithm-- 16022021-0910
U1-Centrality, Degree Centrality, Eigen vector centrality,-18022021-0910
U1- Katz Centrality-22022021-0910
U1- Page rank centrality, Betweeness centrality -23022021-0910
U1- Balance and Status-01032021-0910
U1- Network Models-02032021-0910
U2 Community Analysis, Community Detection Algorithms, Member Based Community Detection
U2 Unit 2 Quetionare and Clique Percolation Method
U2 Node Similarity, Ratio, Normalized cut, Modularity, Normalised Modularity
U2 Modularity Maximization, Heirarchical Communities, Edge Betweeness, GirvanNewman Algorithm, Community Evolution and Evaluation
U2 Precision, Recall, F Measure, Mutual Information, Information Diffusion, Herd Behaviour, Herding All Examples
U2 Information Cascade, Independent Cascade Model, Maximizing the Spread of Cascades, Information Intervention, DIffusion of Innovation Model, Diffusion Case Studies
U2 DIffusion of Innovation Model, Diffusion Case Studies , Epidemics
U3 Data Mining Essentials: Data, Data Processing
U3- Data Preprocessing: Supervised Learning, Classification(Drive Link to the slides)
U3_ Data Preprocessing_ Supervised Learning_Regression_and_Unsupervised Learning
U3_Influence and Homophily_ Measuring Assortativity
U3_Measuring Homophily
U4_Recommedation in Social Media: Challenges, Classical Recommendation Algorithms Part 1
U4_Classical Recommendation Algorithms Part 2, Recommendation Using Social Context, Evaluating Recommendations
U4_Behavior Analytics: Individual Behavior
U4_Behavior Analytics: Individual Behavior
U5_Prediction_for_Future
U5_Prediction_of Learning_&_Predicting_Elections
U5_Prediction_of Box Offices
U5_Predicting_StockMarket_Closing Predictions
Revision Sections & Miscellaneous
Text Preprocessing in Python, Revision in Code Part I
Text Classification with several Machine Learning algorithms in Python, Revision in Code Part II
Assignment I - ( Completed )
Assignment II - ( Completed )
Assignment III - ( Completed )
Slip Test/Assignment IV (Completed)
Please fill the SMA Exit Survey
Question Bank of Random Questions for Reference
Next
CHAITANYA BHARATHI INSTITUTE OF TECHNOLOGY (A)
Side panel
Log in
Username
Password
Remember username
Forgot Password?
Log in
IT1-Social Media Analytics (Ele-3)-VI Sem-2020-2021
Home
Calendar
Skip to main content
Course info
Home
Courses
Information Technology
UG
Academic Year 2020-2021
VI-Semester
IT1-Social Media Analytics (Ele-3)-VI Sem-2020-2021
Summary
IT1-Social Media Analytics (Ele-3)-VI Sem-2020-2021
Teacher:
Ms. K. Swathi Assistant Professor
Teacher:
Sri. R. Sai Venkat Assistant Professor
Skill Level
:
Beginner