• Home
  • About
  • Mobile
  • Open Content
  • Search

Module Overview


  • Description
  • Facilitators
  • Weblinks
  • Timetable
EB5204 

NEW MEDIA AND SENTIMENT MINING
   2018/2019, Semester 1
   Non-Faculty-Based Departments (Institute Of Systems Science)
Modular Credits: 3
  Tags: --

Learning Outcomes

TopAt the end of the course, the participants will be able to:
  1. Identify where sentiment analysis can be applied
  2. Evaluate and analyse the classification techniques for sentiment classification and apply it with open source libraries
  3. Design a sentiment analysis system for customer feedback and reviews
  4. Design a sentiment analysis system for news and social media for applications in finance
  5. Evaluate and assess sentiment analysis at a granular level for entities and aspects

Prerequisites

TopEB5001 Foundations of Business Analytics

Schedule

TopDay 1 October 6 9 am -5pm
Day 2 October 13  9 am -5pm 
Day 3 October 20  9 am -5pm
Day 4 October 27  9 am -5pm
Day 5 November 3  9 am -5pm

Syllabus

TopThe following topics will be covered
  • Introduction to sentiment analysis and its applications in various social domains.
  • Overview of related tasks of NLP to sentiment analysis
  • Supervised learning classification algorithms for sentiment analysis
  • Entity and aspect mining for sentiment analysis
  • Sentiment visualization tools
  • Applications of sentiment analysis to customer analytics and financial applications
  • Sentiment analysis and its psychological basis

Assessment

TopThe assessment will be 50% in examinations and 50% continual assessment.
Continual Assessment – 50%
•2 individual MCQ quizzes (15%)
The 1st quiz on day 4 of the course will test the material largely in the 1st 2 days of class. The 2nd quiz on day 5 of the course largely covers the materials on the 3rd and 4th days. The quizzes will be MCQ with True/ False questions.
•1 group programming assignment (15%)
Students are expected to form groups of 4-6 to submit a programming assignment of a real-use example.
•1 group presentation topic (10%) and report/ programming (10%)
Students are to present 15 mins on a sentiment-related topic (10%) and submit a final report (10%) .

Late submissions of the CA will result in a penalty of 25% of the marks for each day late.


Final Exam – 50%
•3 sections:
•Section A: 10 marks (by Sam Gu)
─General topics on sentiment analytics
•Section B: 28 marks (by Eric)
─Classification algorithms for sentiment mining & entity mining
─Applications in financial analytics
•Section C: 12 marks (by Dr. Rakesh)
─Applications in customer analytics

The final scores allocation may be subject to minor amendments.

Preclusions

TopNIL

Workload

Top1.5-0.5-0.5-3-2

Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week

Contact

  • IVLE Webmaster

Social Media

Latest Alerts

  • IVLE scheduled maintenance every Tuesday 0300 hrs - 0700 hrs

Centre for Instructional Technology

Legal  |  Acceptable Use Policy

Copyright © 2015, National University of Singapore. All rights reserved.