CSC 5993-001 Sentiment Analysis Spring 2018

SYLLABUS


Meetings
Meeting schedule tba (Mendel 160A)
Instructor Dr. Tom Way
160A Mendel Science Center
Email:   thomas.way@villanova.edu
Skype:  DrTomWay
Office hours (See my web site)
Teaching Assistant TBA
Textbook None. We will use online resources.
Web Site
http://www.csc.villanova.edu/~tway and follow the link for CSC 5993
Catalog Description

In depth exploration of Sentiment Analysis. Natural language processing theory and practice for applications to sentiment analysis and opinion mining. Related topics in machine learning, Python programming and use of the Natural Language Toollkit. Students will develop numerous Sentiment Analysis tools and applications, conduct short-term research projects in Sentiment Analysis, and produce a write-up of their results.

Course Description

This independent study course explores the relatively recent field of Sentiment Analysis, a specialized form of natural language processing that attempts to reveal opinions expressed in written text. The course uses a project-based approach, with individual projects designed to introduce each topic or sub-topic through study and then implementation of ideas learned. Student progress will be assessed through individual project demonstrations and discussions, and through the completion of a research paper that summarizes the results of research conducted using the implemented projects.

Learning Objectives This course is an introduction to Sentiment Analysis, with topics covered including relevant aspects of machine learning and Python programming.
  • Establish an understanding of concepts and theories of Sentiment Anslysis.
  • Establish an understanding of related concepts in machine learning, data mining, and natural language processing.
  • Establish proficiency in Python programming, and specifically the use of the Natural Language Toolkit to solve problems in Sentiment Analysis.
  • Establish an understanding of research approaches in Sentiment Analysis through conducting experiments and writing up results in research paper form.
Grading Policy
Grading will be based evaluation of projects, write-ups and individual exams.

25%  Projects
25%  Individual, informal oral examinations and discussions
50%  Research experimentation and write-ups

Final Grades
94 A 88 B+ 78 C+ 68 D+
90 A- 84 B 74 C 64 D
80 B- 70 C- 60 D-
Makeup Policy
No missed or late assignments, exams or projects without prior excuse. Each case will be handled separately based on its own merits. Each student is responsible for what is covered and assigned in any classes which they miss. Abuse of this policy will result in a loss of leniency.
Academic Integrity Students will be expected to use good judgment in following the University's policy on Academic Integrity. Severe academic penalties will be imposed for violations of this policy, such as receiving at a minimum 0% credit for an assignment, or at the maximum a failing grade for the course, at the discretion of the instructor.

Last updated: 01/15/2018