TDDE16 · 6 credits · Advanced level (A1F) · Informationsteknologi, Datateknik, Datavetenskap
Text Mining
Active · next start HT 2026
94%
passed the latest regular sitting
LAB1 · 17 Jan 2026 · 48 students
85%
passed the regular sittings, all sittings over the last 5 years
LAB1 · 247 students
42%
Harder than 42% of courses at advanced level
Compared with 285 courses at the same level, ordinary exams over the last three academic years. 76 students.
Part of 6 programmes
- Master of Science in Computer Science and Engineering
- Master of Science in Information Technology
- Master of Science in Computer Science and Software Engineering
- Master of Science in Applied Physics and Electrical Engineering - International
- Master of Science in Applied Physics and Electrical Engineering
- Master's Programme in Computer Science
Hand-in or lab
LAB1 Laboratory exercises
- G
- U
Main rounds
85% passed · 247 students
- 94%48
- 79%28
- 75%59
- 82%62
- 98%50
Later rounds
100% passed · 7 students
- 100%7
- Pass a later round: 100% (7 students), compared with 85% (247 students) on the ordinary exam
Show small sittings (9)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%3
- 100%4
- 100%1
- 100%1
- 100%1
- 100%1
- 0%1
- 50%2
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
PRA1 Project
- 5
- 4
- 3
- U
Main rounds
73% passed · 172 students
- 82%39
- 74%19
- 62%45
- 78%32
- 70%37
Later rounds
82% passed · 74 students
- 88%8
- 83%6
- 67%6
- 100%19
- 83%6
- 75%8
- 60%5
- 75%16
- Pass a later round: 82% (74 students), compared with 73% (172 students) on the ordinary exam
- Get 4 or 5: 46% (246 students)
Show small sittings (5)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%2
- 100%1
- 100%1
- 100%4
- 50%2
About the course
Aim
Text Mining develops methods for accessing information in and extracting knowledge from large volumes of text. The overall aim of this course is to provide students with practical experience of the main steps of text mining: information retrieval, processing of text data, modelling, analysis of experimental results. The course ends with an individual project where students work on a self-defined problem.
On completion of the course, the student should be able to:
- implement text mining methods and apply them to practical problems
- analyse and summarise results from text mining experiments
- identify, formulate and solve problems within the area of text mining
- clearly present and discuss the conclusions of a project work
Content
- information retrieval
- basic natural language processing
- predictive modelling, in particular text classification
- text clustering and topic modelling
- information extraction
- validation methods
- applications of text mining
- software libraries and data
- Examiner
- Marcel Bollmann
- Scheduled hours
- 24 h
- Main field of study
- Informationsteknologi, Datateknik, Datavetenskap