Text Analytics:
95-865 (A)
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This page is for Section A (Pittsburgh).
The page for Section K (Adelaide) is here.

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: Fall Mini A2, Tu/Th 4:30 - 5:50, HBH 1502
Instructor: Jamie Callan
Teaching Assistants: Fangyuan (Sophie) Cao (fangyuac@andrew), Rayan Cornelio (rcorneli@andrew), and Amar Wasan (awasan@andrew)
Office hours:
Monday 12:00-1:30 Amar Heinz Cafe
Tuesday   1:30-3:00 Rayan Heinz Cafe
Wednesday (Nov 11 only)
Friday (except Nov 13)
12:00-1:30 (Nov 11 only)
10:30-12:00 (except Nov 13)
Fangyuan (Sophie) Heinz Cafe
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: Some lectures have assigned readings from Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze, Cambridge University Press. 2008. The links next to each lecture provide access to an online version of the text.

Some lectures have assigned readings from other papers, as shown in the link next to the lecture.

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
Oct 27 Course overview and introduction to text analytics (pdf)  
Oct 29 Exploratory analysis: Frequency analysis (pdf) Ch 2.0 - 2.2
Nov 3 Exploratory analysis: Co-occurrence analysis (pdf)
HW1 out
Nov 5 Exploratory analysis: Clustering (pdf) Ch 16
Nov 10 Exploratory analysis: Clustering (pdf) Ch 17
Nov 12 Predictive analysis: Categorization (pdf)
HW1 due, HW2 out
Ch 14.0-14.3
Nov 17 Predictive analysis: Categorization (pdf) Ch 13
Nov 19 Predictive analysis: Categorization (pdfa, pdfb) Ch 15.0-15.3
Nov 24 Predictive analysis: Categorization (pdf)
Predictive analysis: Sentiment analysis
HW2 due, HW3 out (pdf, docx)
Dec 1 Predictive analysis: Sentiment analysis  
Dec 3 Tools: Search engines as language databases
HW3 due
Dec 8 Case studies: Expert finding, E-Discovery  
Dec 10 Final exam  

Copyright 2015, Carnegie Mellon University.
Updated on November 24, 2015
Jamie Callan