For mining information from very large repositories of text, you need to know what you are looking for and how to find it. The Master’s programme Text Mining trains everyone with a Bachelor’s and or Master’s degree how to use text mining by applying Artificial Intelligence (AI) techniques. You will learn what the possibilities and limitations are, and how to reflect on the outcomes and the cross-disciplinary aspects.

What is Text Mining?
Did you ever wonder why there is such a need for finding information in text by using Artificial Intelligence techniques? Scientists and decision makers are very much in need of the information that must be somewhere in textual big data. Some examples:

  • millions of medical reports and scientific papers on specific symptoms are hiding loads of information, waiting to be discovered;
  • hundreds of thousands of documents on economic policies produced by (non) governmental institutions;
  • emotions, opinions and mental states of all kinds of people on all kinds of topics can be found in abundance at the internet and in social media;
  • historical archives, libraries with centuries of books are now available digitally, waiting to be explored through new techniques.

You probably never realized, but most big data is textual and the amount of textual information available in digital form is still rapidly growing. Although language is very accessible for humans, it is not for machines. That massive amount of information is very hard to explore! That is where text mining comes in.

Text mining is trying to extract knowledge, information and structured data from text and utterances by using a collection of software and Artificial Intelligence methodologies. Although text mining is grounded in linguistics, computer science and Artificial Intelligence, it is extremely relevant for almost all fields of knowledge.

Study Programme

As a bachelor or master in your field of knowledge you know what kind of information you could be looking for. With the Master’s programme Text Mining you will become a pioneer in the analysis of textual big data with Artificial Intelligence techniques. You bring your field knowledge, we teach you the tools and techniques.

Cross disciplinary
Text mining is cross-disciplinary in a true sense. There are enough tools available on the internet, but it requires insight, knowledge and experience to use this technology properly for a specific purpose and to understand the outcome. Experts in text mining must have knowledge from both linguistics and technical aspects. Furthermore, they need to understand the field of knowledge in which they are applying these techniques.

Study programme
In the first period you will learn the basics of linguistics and programming in Python for Text Analysis.

The second period brings you more specialized knowledge and skills: we will show you how to look at language as data and you will learn to use methods and tools for the processing of language in the course NLP Foundations.

In period three and four you will really dive into text mining. In Applied Text Mining we will give you hands-on experience to build what we call a reading machine. In Text Mining in Domains you will apply your newly acquired knowledge in your professional domain. At the same time you will follow a course in Machine Learning, especially for working with language.

You will write your thesis in the last two periods, most likely based on an internship.

Career prospects

Worldwide there is an extremely large amount of digitally available text, yet it is hard to find the right piece of information that is relevant for a specific situation.

There is a fast-growing need for specialists that can apply text mining, turn it into a product and exploit the results in industry, governmental and non-governmental organizations (NGOs).

This Master’s programme offers advanced students a scientific challenge, as well as a great opportunity to apply their field competence in a rapidly growing field of science and industry.

Admission requirements

The Master’s programme Text Mining is open for application to students with an academic Bachelor's Degree (or equivalent) in Linguistics.

Students who do not have a Bachelor’s Linguistics degree and wish to enroll for the Master Text Mining are invited to submit a request to the Faculty’s Admissions Board. In response to this request, the Admissions Board will indicate what knowledge, skills and insights the student must have in order to be admitted to the programme.

The knowledge, skills, and insights the student must have in order to be admitted to the programme includes knowledge of linguistic concepts in the fields of phonology, morphology, syntax, semantics and language variation; the ability to observe and analyze phonological, morphological, syntactic and semantic structures in typologically different languages; and insights into the role of linguistics in language therapy, language counselling, language policy and in other social contexts. This knowledge, skills and insights can be acquired in, for instance, a course Introduction to Linguistics of an academic bachelor's programme.

The Faculty’s Admissions Board will review whether you meet the admission requirements.

English language requirements
Students applying for a Master’s degree programme should be able to speak, read, write and understand English at an advanced academic level.

The proficiency requirement in English can be met by the successful completion of one of the examinations mentioned below, with the scores indicated. Only students who have completed a full high school/International Baccalaureate in English or Bachelor’s degree in Canada, USA, UK, Ireland, New Zealand, or Australia may be exempted.

  • Cambridge English: Cambridge Proficiency Exam A, B, C, or Cambridge Advanced Exam A, B, C
  • IELTS: 7.0 overall band score, with a minimum of 6.5 on each item
  • TOEFL score:
    paper based: 600, with a minimum of 55 in each of the subtests plus 4.0 in TWE
    internet based: 100, with a minimum of 20-23 in each of the subtests

Please note: tests must have been completed no more than two years before the start of the programme.

Application

If you have read the admission criteria and feel you are eligible for admission, please take the following steps to submit your application. Note that the initial application procedure is fully online and that scans of your relevant documents are required.

You can find all information on the application procedure on the Admission and Language Requirements page.

Step 1: Meet admission criteria

Step 2: Prepare documents and apply online
Please prepare the following documents. You can find an explanation of each document on the application page. All documents should be provided in English.

  • Copy of your valid passport or ID (ID only for EU residents)
  • Curriculum Vitae
  • Motivation Letter
  • Transcript of records
  • Thesis (or another sample of academic writing, at least 5 pages plus a list of used literature)
  • A description of the relevant courses you have taken during your previous higher education and a list of all the main literature

After having prepared the required documents, please follow the online application procedure. After you have completed the application, our international student advisors will contact you via email.

Step 3: Await decision on admission
On behalf of the Examinations Board, the Faculty’s Admissions Board will review your application. Normally this takes about four weeks, but it might take longer in busy periods so be sure to apply as soon as possible. If you gain admission, you will receive a letter of conditional admission by email.

Step 4: Finalize your registration and move to Amsterdam!
Make sure to finalize your registration as a student before the start of the programme. On the Admission and Language Requirements page you will find an explanation what to do after admission. When all conditions are met you will be ready to start your programme at VU Amsterdam!

Overview Linguistics: Text Mining.

  • Language of instruction: English
  • Duration: 1 year
  • Application deadline: 1 June for Dutch and EU-students. 1 April for non-EU-students.
  • Start date: 1 September
  • Study type: Full-time
  • Field of interest: Computer Science, Mathematics and Business, Language and Communication

Program taught in:
  • English

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Last updated October 8, 2019
This course is Campus based
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Duration
1 semester
Full-time
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2,083 EUR
- EU/EEA student; 14,600 EUR - Non-EU/Non-EEA student
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