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2019/2020  KAN-CDASO2030U  Text Analytics

English Title
Text Analytics

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory offered as elective
Level Full Degree Master
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Raghava Rao Mukkamala - Department of Digitalisation
Main academic disciplines
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 03-07-2019

Relevant links

Learning objectives
  • Characterize the phenomena of text analytics
  • Summarize different fundamental concepts, techniques and methods of text analytics
  • Analyze and apply different text analytics techniques for big/business datasets in organizational contexts
  • Understand the linkages between business intelligence and text analytics and the potential benefits for organizations
  • Critically assess the ethical and legal issues in applying text analytics
  • Summarize the application areas, trends, and challenges in text analysis
  • Exhibit deeper knowledge and understanding of the topics as part of the project and the report should reflect on critical awareness of the methodological choices with written skills to accepted academic standards
Course prerequisites
This course requires a fundamental understanding of programming in Python language as achieved in, or comparable to, Foundations of Business Data Analytics: Architectures, Statistics and Programming course from 1st semester of Cand.Merc.IT (Data Science).
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved: 2
Compulsory home assignments
Each assignment is 1-3 pages in group of 1-4 students.
The students have to get 2 out of 4 assignments approved in order to go to the exam.

There will not be any extra attempts provided to the students before the ordinary exam.
If a student cannot hand in due to documented illness, or if a student does not get the activity approved in spite of making a real attempt, then the student will be given one extra attempt before the re-exam. Before the re-exam, there will be one home assignment (max. 10 pages) which will cover 2 mandatory assignments.
Text Analytics:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance.
Individual or group exam Individual oral exam based on written group product
Number of people in the group 2-4
Size of written product Max. 15 pages
Assignment type Project
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and external examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Students can submit the same project or they can choose to submit a revised project.
Course content, structure and pedagogical approach

The course provides knowledge of various concepts, techniques and methods related to text analytics. Furthermore, it introduces


  • Basics of Natural Language Processing (NLP) such as POS-tagging, Entity recognition
  • Language Modeling using N-grams
  • Text classification using supervised machine learning approaches
  • Unsupervised methods for NLP and latent models.
  • Word-embeddings and Word Vectors
  • Neural Networks for NLP and Neural Language Models
  • Information Retrieval
  • Relation Extraction, Question Answering, Dialog systems and Chatbots

  • Web Crawling and Link Analysis


Furthermore, the course provides the students with practical hands-on experience on text analytics using open source machine learning libraries such as scikit-learn, Natural Language Toolkit (NLTK) in Python programming language. After completing the course the students will be able to apply and use various NLP techniques such as sentiment/emotion analysis opinion mining etc. on textual documents/ text corpora.

Description of the teaching methods
The course consists of lectures, exercises, and assignments. Each lecture is followed by an exercise session, and there will be a teaching assistant providing technical support for assignments and course projects.

The presented theories, concepts and methods should be applied in practice and exercise sessions. The students work in the entire semester on a mini project displaying the understanding of the concepts presented in the lectures and exercises. CBS Learn is used for sharing documents, slides, exercises etc. as well as for interactive lessons if applicable.
Feedback during the teaching period
Feedback on mandatory assignment in general
Student workload
Lectures 24 hours
Exercises 24 hours
Prepare to class 48 hours
Project work & report 100 hours
Exam and prepare 10 hours
Total 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy the books.



  • Jurafsky, D., & Martin, J. H. (2009). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River, N.J: Pearson Prentice Hall.
  • Christopher D. Manning and Hinrich Schütze. 1999. Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, MA, USA.

Notes, articles, chapters and webpages will be handed out/made available during the course

Last updated on 03-07-2019