Text Analytics:
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Description: Many organizations need to analyze large amounts of text to discover useful information. For example, a company may want to monitor how the public discusses its products in social media, or a forensics team may need to discover the contents of disk drives seized by law enforcement. This course provides students with an understanding of common and emerging methods of organizing, summarizing, and analyzing large collections of unstructured and lightly-structured text ('text analytics'). The focus is on algorithms and techniques, however the course also provides an introduction to open-source software tools
This is a 6 unit course. It is offered during the second half of the Fall (Mini-2) and Spring (Mini-4) semesters.
Learning Objectives: By the end of the course, students are expected to have developed the following skills. Skills are assessed by the homework assignments and the final exam.
  • Recall and discuss common methods of conducting exploratory and predictive analysis of text information;
  • Use search engines and common open-source software to perform common methods of exploratory and predictive analysis; and
  • Apply text analysis techniques discussed in class to solve problems faced by governments and companies;
Prerequisites: None
Time & Location: Spring Mini A4, Tu/Th 10:30 - 11:50, HBH 1502
Instructor: Jamie Callan
Teaching Assistants: Shubham Jaiswal, Zhitao Pei, and Amar Wasan
Office hours: TBD
Discussion Forum: A discussion forum is provided for students to ask questions, answer questions, and discuss class-related topics. You will need a Piazza account to use the discussion forum. Please provide a CMU email address when you join the 95-865 discussion (you can use other email addresses, too). We will periodically remove students that do not have CMU email addresses.
Instructional Materials: Online access to some materials is restricted to the .cmu.edu domain. CMU people can get access from outside .cmu.edu (e.g., from home) using CMU's WebVPN Service.
Homework: 3 assignments that give hands-on experience with techniques discussed in class.
Grading: 3 assignments (3 x 25%) and a final exam (25%).
Grading Scale: Grades are assigned using a curve.
Course policies: Attendance, Auditing, Laptops & mobile devices, Late homework, Pass/fail, Plagiarism & cheating, Recording & videotaping , Waitlist
Syllabus (subject to revision):
Date Topic Reading
Mar 17 Course overview and introduction to text analytics  
Mar 19 Text representation: Turning text into features  
Mar 24 Exploratory analysis: Frequency and co-occurrence  
Mar 26 Exploratory analysis: Co-occurrence and clustering
HW1 out
Mar 31 Exploratory analysis: Clustering  
Apr 2 Predictive analysis: Categorization
HW1 due, HW2 out
Apr 7 Predictive analysis: Categorization  
Apr 9 Predictive analysis: Categorization  
Apr 14 Predictive analysis: Sentiment analysis Feldman
Apr 21 Predictive analysis: Sentiment analysis
HW2 due, HW3 out
Apr 23 Tools: Search engines as language databases  
Apr 28 Case studies: Expert finding  
Apr 30 Case studies: E-Discovery
HW3 due
TBD Final exam Sample final

Copyright 2014, Carnegie Mellon University.
Updated on January 12, 2015
Jamie Callan