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. 

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.  

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.