This course introduces the basic and advanced concepts and ideas in text mining and natural language processing. In this course, students will learn how to apply text mining methods on text data and analyse them in a pipeline with machine learning and deep learning algorithms. The course has a strongly practical hands-on focus, and students will gain experience in using text mining on real data from social sciences, humanities, and healthcare and interpreting the results.
Given the rapid rate at which text data are being digitally gathered in many domains of science, there is a growing need for automated tools that can analyze, classify, and interpret this kind of data. Text mining techniques can be applied to create a structured representation of text, making its content more accessible for researchers. Applications of text mining are everywhere: social media, web search, advertising, emails, customer service, healthcare, marketing, etc. This course offers an extensive exploration into text mining with Python. The course has a strongly practical hands-on focus, and students will gain experience in using text mining on real data from for example social sciences and healthcare and interpreting the results. Through lectures and practicals, the students will learn the necessary skills to design, implement, and understand their own text mining pipeline. The topics in this course include preprocessing text, text classification, topic modeling, word embedding, deep learning models, and responsible text mining
The course deals with:
- Review the fundamental approaches to text mining
- Understand and apply current methods for analyzing texts
- Define a text mining pipeline given a practical data science problem
- Implement all steps in a text mining pipeline: feature extraction, feature selection, model learning, model evaluation
- Understand and apply state-of-the-art methods in text mining
- Implement word embedding and advanced deep learning techniques
The course starts with reviewing basic concepts of text mining and implementing advanced concepts in natural language processing. At the end of the week, participants will master advanced skills of text mining with Python.
Participants should have a basic knowledge and a motivation of scripting and programming in Python.
Participants are requested to bring their own laptop computer. Software will be available online.
This course can be taken separately, but is also part of a series of seven courses in the Summer School Data Science specialisation taught by the Utrecht University department of Methodology & Statistics:
- Data Science: Programming with Python (Course code S17, 3-7 July 2023)
- Data Science: Statistical Programming with R (Course code S24, 3-7 July 2023)
- Data Science: Multiple Imputation in Practice (Course code S28, will not be available in 2023)
- Data Science: Data Analysis (Course code S31, 10-14 July 2023)
- Data Science: Network Science (Course code S37, 10-14 July 2023)
- Data Science: Introduction to Text Mining with R (Course code S41, 10-13 July 2023)
- Data Science: Applied Text Mining (This course)
Upon completing, within five years, three out of seven courses in the Summer School Data Science specialisation (no more than one text mining course), students can obtain a certificate.
Please see here for more information about the full specialisation.
Target audience
This course works best for learners who are comfortable programming in Python, want to acquire skills in text mining approaches, and have a basic knowledge of machine learning.
Participants should also have a basic knowledge and a motivation of scripting and programming in Python. Participants from a variety of fields, including sociology, psychology, education, human development, marketing, business, biology, medicine, political science, and communication sciences, will benefit from the course. A maximum of 80 participants will be allowed in this course. Please note that the selection for this course will be done on a first-come-first-served basis.
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
Aim of the course
The course teaches students the basic and advanced text mining techniques using Python on a variety of applications in many domains of science. The skills addressed in this course are:
- Python environment;
- Preprocessing text and feature extraction;
- NLTK, Gensim, spaCy;
- Text classification;
- Sentiment classification;
- Text clustering;
- Topic modeling;
- Word embedding;
- CBOW vs Skip-gram;
- Convolutional neural networks;
- Recurrent neural networks;
- Attention models;
- Responsible text mining;
- Text summarisation.
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
Study load
Five full days. A typical course day starts at 9.00 and ends at 17.00 with breaks for coffee, lunch and tea.
Please note that there are no graded activities included in this course. Therefore, we are not able to provide students with a transcript of grades. You will obtain a certificate upon completion of this course.
Costs
Tuition fee for PhD students from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.
There are no scholarships available for this course.
Additional information
We also offer tailormade M&S courses and in-house M&S training. If you want to look the possibilities, please contact us at ms.summerschool@uu.nl
Application
Please include a short description about your (scientific) background, and what you expect to learn from this course (or would like to learn).
Contact details
Team M&S Summer School | E: MS.summerschool@uu.nl