Text Mining, 6 credits

Text Mining, 6 hp

732A81

Main field of study

Statistics

Course level

Second cycle

Course type

Single subject and programme course

Examiner

Marcel Bollmann

Course coordinator

Marcel Bollmann

Director of studies or equivalent

Jonas Kvarnström
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Weeks Language Campus ECV
Single subject course (, ) Autumn 2025
F7MML Statistics and Machine Learning, Master´s Programme - First and main admission round 3 (Autumn 2025) E
F7MML Statistics and Machine Learning, Master´s Programme - Second admission round (open only for Swedish/EU students) 3 (Autumn 2025) E

Main field of study

Statistics

Course level

Second cycle

Advancement level

A1F

Course offered for

  • Master's Programme in Statistics and Machine Learning
  • Master Programme in Cognitive Science

Entry requirements

  • 180 ECTS credits passed including 90 ECTS credits in one of the following subjects:
    • statistics
    • mathematics
    • applied mathematics
    • computer science
    • engineering
  • Passed courses in:
    • calculus
    • linear algebra
    • statistics
    • programming
  • English corresponding to the level of English in Swedish upper secondary education (Engelska 6)
    Exemption from Swedish
  • At least 30 ECTS credits passsed from semester 1 and 2 Master's Programme in Statistics and Machine
    Learning, including the course Machine Learning 9 ECTS credits, or equivalent

Intended learning outcomes

After completion of the course the student should on an advanced level be able to: 

  • implement text mining methods and apply them to practical problems
  • analyze and summarize 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
     

Course content

The course covers the following methods within the area of text mining:

  • information retrieval
  • basic methods in language technology
  • predictive modeling, in particular text classification
  • text clustering and theme modeling
  • information extraction
  • validation methods

In addition, various applications of text mining as well as program libraries and data used in the field are treated.

Teaching and working methods

The teaching consists of lectures, computer labs and supervision in connection with a project work. In addition, the student must engage in self-studies.

Language of examination: English

The student is entitled to supervision during the study period to complete the project work for the current course that the student is registered to.

Examination

The course is examined by:

  • written and oral presentation of computer labs in groups, grading scale: UG
  • individually written project report, grading scale: EC

For Pass (E) as a final grade, Pass (G) is required on the computer labs and at least E on the individually written project report. A higher grade is based on the individually written project report.

If special circumstances prevail, and if it is possible with consideration of the nature of the compulsory component, the examiner may decide to replace the compulsory component with another equivalent component.

If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it.

If the coordinator has recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.

An examiner may also decide that an adapted examination or alternative form of examination if the examiner assessed that special circumstances prevail, and the examiner assesses that it is possible while maintaining the objectives of the course.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.

Grades

ECTS, EC

Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is conducted in such a way that there are equal opportunities with regard to sex, transgender identity or expression, ethnicity, religion or other belief, disability, sexual orientation and age.

If special circumstances prevail, the vice-chancellor may in a special decision specify the preconditions for temporary deviations from this course syllabus, and delegate the right to take such decisions.

Department

Institutionen för datavetenskap
Code Name Scope Grading scale
PRA1 Examination 3 credits EC
LAB1 Laboratory work 3 credits EC
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