Master of Digital Text Analysis (M.A.)
DURATION
1 Years
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
Request application deadline
EARLIEST START DATE
Sep 2025
TUITION FEES
EUR 979 / per year *
STUDY FORMAT
On-Campus
* 1116€ for EEA nationals | 5,800€ for non-EEA nationals
Introduction
Digital text is ubiquitous in contemporary society. Modern computational technology enables exciting applications in both research and the industry. In this Master of Digital Text Analysis, you will acquire all critical and computational skills that are nowadays expected of experts in digital text analysis.
The curriculum is unique in its emphasis on data-heavy approaches to language and text from a Humanities perspective. Starting from scratch, the modules cover a wide array of text technologies, ranging:
- from artificial intelligence (machine learning)
- over data science (statistics)
- to natural language processing (computational linguistics)
This ambitious programme seeks to prepare the next generation of highly self-reliant, culture-aware experts in text analytics, who are highly employable in a variety of research contexts across academia and the industry, including the cultural sector.
Quality assurance
At the University of Antwerp, we ensure the quality of our programmes. Every programme goes through a six-year quality assurance cycle.
A peer review in year 6 concludes this cycle. The study programme conducts a self-reflection has discussions with internal and external experts, and with an independent student.
On 14 and 15 November 2018 the peer review team visited the Master of Linguistics and decided to confirm its confidence in the programme.
Most important conclusions of the peer review
The lecturers have a critical attitude to constantly improving the programme and its position in the higher education market. The study programme stimulates students to follow their interests as much as possible. Therefore at the moment of the peer review, three specialisation options were offered in the Master of Linguistics: (1) Language in use (2) Computational psycholinguistics and (3) Digital Text Analysis. In this last option, a bridge is built between linguistics and literary studies.
This important new development of digitisation receives much-needed attention in the programme, partly due to the lecturers who are leading experts in digitisation in literature and language studies.
The peer review team concludes that the master's theses are at a good level. All master's students of Linguistics can take up a professional or a research internship. The Master of Linguistics is particularly notable for its strong research orientation, which is important to recruit doctoral students.
The Education Committee will further strengthen the internationalisation of the programme by developing structural international partnerships and removing barriers for exchange, such as the burden of administrative formalities and obtaining equivalencies for courses taken abroad. The Education Committee is also implementing blended learning. Although students are formally represented in councils and committees, the study programme is committed to further strengthening the participation of and communication with students. On the advice of the peer review team, the education committee will reflect critically on how the specialisations in the Dutch spoken Master taal- en letterkunde relate to the specialisation options in the Master Linguistics. It will examine to what extent it is meaningful and useful that both master's programmes and similar options continue to be offered side by side. In view of increasing student numbers, the education committee will consider setting up an English programme.
Gallery
Admissions
Scholarships and Funding
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Curriculum
What's Digital Text Analysis?
Starting from scratch, the curriculum covers a wide array of text technologies.
- Natural Language Processing: Technologies from computational linguistics are nowadays crucial in digital text analysis. This module offers an extensive introduction to modern pipelines in Natural Language Processing. Jointly, we work towards practical applications on textual data, as well as solutions for the many challenges that remain open in the field.
- Data Science: Employers in academia and the industry increasingly expect data scientists to be able to rapidly extract relevant insights from large document collections. In this module, we focus on exploratory data analysis and practical data visualization, involving challenging, real-world datasets.
- Machine Learning: Deep learning features prominently in this programme, as a crucial component of contemporary artificial intelligence. Modules focusing on neural networks are supplemented by a wide-ranging and in-depth survey of established, alternative methods from machine learning.
Bootcamp
A unique feature of this curriculum is the compulsory programming boot camp at the beginning of the first semester. This 3-week course makes no assumptions whatsoever about the students’ coding proficiency and will provide a no-nonsense introduction to modern computer programming and the wider scientific ecosystem in computing.
Hands-on
Above all, this Master's delivers a practice-oriented educational programme, for example through a project-based Master’s thesis and the possibility of an external internship with one of our many stakeholders in academia or the industry. On top of these, students can choose from a diverse array of elective modules from the departments of literature and linguistics.
Rankings
UAntwerp, which was founded in 2003, scores extremely well in Young University Rankings
- Times Higher Education Millennials 2020 - Rank 5
- Times Higher Education Young University Ranking 2024 - Rank 12
- QS 50 under 50 2021 - Rank 20
Read more about our rankings on our website
Program Outcome
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Program Tuition Fee
Career Opportunities
The focus of this Master resembles that of a Research Master and will deliver ambitious, well-skilled students that are perfectly equipped for a PhD track, for instance, in computational humanities or allied domains. Many students will take up careers as research/data scientist in the (tech) industry, where they’ll work on analysing all sorts of textual data, such as customer data, likely in the context of machine translation, dialogue systems or recommendation engines.
Finally, we expect many of our students to find opportunities in the cultural or GLAM sector (galleries, libraries, archives, museums), where employees combining a passion for computing technology and culture are highly sought-after.