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Text Mining 7.5 credits

Text Mining
Second cycle, D7058E
Course syllabus valid: Autumn 2021 Sp 1 - Present
The version indicates the term and period for which this course syllabus is valid. The most recent version of the course syllabus is shown first.

Education level
Second cycle
Grade scale
Information Technology
Subject group (SCB)
Computer Technology
Main field of study
Information Systems Sciences, Computer Science and Engineering

Entry requirements

In order to meet the general entry requirements for the course, you must have accomplished a minimum of 180 ECTS of university studies, out of which 60 ECTS in the areas of computer or system science. The studies shall have included Introductory Programming (for example D0009E or D0007N) and Fundamentals of Databases (for example D0004N or D0018E). The Text Mining course also requires a basic data mining course (for example D0025E Data Mining). Good knowledge in English equivalent to English 6. More information about the English language requirements [].

More information about English language requirements


The selection is based on 20-285 credits

Course Aim

The objective of the course is for the student to develop their knowledge and skills in Text Mining. After passing the course, the student should be able to:

[1].   Explain and use text preprocessing techniques

[2].   Describe a text analytics system together with its components, optional and mandatory ones

[3].   Explain how text could be analyzed

[4].  Evaluate results of text analytics

[5].   Analyze and reflect on the various techniques used in text analytics and the parameters needed as well as the problem solved   

[6].   Plan & execute a text analytics experiment 


The Text Mining course is focusing on the importance and the difficulty of analyzing text. The Text Mining course is designed to provide students with knowledge relevant to both preprocessing of text as well as analytics of text. The Text Mining course, however, focuses on wide range of algorithms, techniques, and tools. These include standard methods, such as: tokenization, TF-IDF, n-grams, Named Entity Extraction, Sentiment Analysis, and Topic Modeling. Furthermore, recent trends in machine learning and deep learning are also covered, including: Word2Vec, Semantic Hashing, and Recurrent Neural Networks for Natural Language Processing. Various examples and use cases are used across the course. 

Each course occasion´s language and form is stated and appear on the course page on Luleå University of Technology's website.

Lectures, labs, assignments, case studies and/or project work. During the course, the students work with individual tasks and/or group work. Some assignments or case studies in the course might contain work in contact with or about the industry. The student uses different methods and techniques, and it is important to choose the right method, technique or computer support for each task. Before and after the tasks are solved, there are lectures to present and discuss different solutions. 

Teaching is in English and on the Internet for distance students or on campus for students living here. IT support: Learning management system, e-mail and phone.  The learning management system is used for delivering course material, information and submissions. Knowledge is shared and created within the course through virtual meetings with teachers and other students for discussions, supervision, teamwork and seminars. For students on campus, there will be meetings on campus.  

If there is a decision on special educational support, in accordance with the Guideline Student's rights and obligations at Luleå University of Technology, an adapted or alternative form of examination can be provided.

Through written tests, individual and group/project assignment, different student abilities are examined. Those are: the ability to explain and use text mining techniques and the ability to address business problems using text mining individually and in groups.


  Technical Requirements: access to computer with administrative rights, web camera, microphone and Internet connection.  

Marcus Liwicki

Literature. Valid from Autumn 2020 Sp 1 (May change until 10 weeks before course start)
Title: Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence (Marketing Science)
Author: Steven Struhl
Publisher: Kogan Page, 1st edition, 2015
ISBN: 978-07499474010

Title: Deep Text: Using Text Analytics to Conquer Information Overload, Get Real Value from Social Media, and Add Bigger Text to Big Data
Author: Tom Reamy
Publisher: Information Today Inc, 2016
ISBN 9781573875295

Title: Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS
Author: Goutam Chakraborty, Murali Pagolu, & Satish Garla
Publisher: SAS Institute, 2014
ISBN 9781612905518

Course offered by
Department of Computer Science, Electrical and Space Engineering

CodeDescriptionGrade scaleHPStatusFrom periodTitle
0002Individual taskU G#1.50MandatoryA20
0003Group-/Project workU G#2.00MandatoryA20
0004Written exam/Individual examU G VG4.00MandatoryA21

Study guidance
Study guidance for the course is to be found in our learning platform Canvas before the course starts. Students applying for single subject courses get more information in the Welcome letter. You will find the learning platform via My LTU.

Syllabus established
by Jonny Johansson, HUL SRT 21 Feb 2020

Last revised
by Jonny Johansson, HUL SRT 17 Feb 2021