Text Analytics:
95-865 (A)
 
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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 1206
Instructor: Jamie Callan
Teaching Assistants: Stefan Hermanek (shermane@andrew)
Kale Malavika Makarand (kmakaran@andrew)
Office Hours:
Day Time Location TA
TBD TBD TBD 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: 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
Mar 21 Course overview and introduction to text analytics  
Mar 23 Exploratory analysis: Frequency analysis
HW1 out
Ch 2.0 - 2.2
Mar 28 Exploratory analysis: Co-occurrence analysis  
Mar 30 Exploratory analysis: Clustering
Homework guidelines
Ch 16
Apr 4 Exploratory analysis: Clustering
HW1 due
Ch 17
Apr 6 Predictive analysis: Categorization
HW2 out
Ch 14.0-14.3
Apr 11 Predictive analysis: Categorization Ch 13
Apr 13 Predictive analysis: Categorization Ch 15.0-15.3
Apr 18 Predictive analysis: Sentiment analysis
HW2 due, HW3 out
Feldman
Apr 20 Predictive analysis: Sentiment analysis  
Apr 25 Tools: Search engines as language databases  
Apr 27 Case studies: Expert finding
Review
HW3 due
 
May 2 Case studies: TBD
Review
HW3 due
 
May 4 Final exam Sample final 1,
Sample final 2
Advice From The Faculty:

This course is a lot of work. Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.

If you find yourself struggling with the material or workload, please ask for help. All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.


Copyright 2016, Carnegie Mellon University.
Updated on January 11, 2017
Jamie Callan