This course introduces the basic concepts of text analysis in Python. In this course, participants will learn how to apply text mining methods on text data and analyse them in a pipeline with machine learning and natural language processing algorithms.
The course has a strongly practical hands-on focus, and participants will gain experience in using text mining on real data.
Given the rapid rate at which text data are being digitally gathered in many domains of science, there is 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 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. Through lectures and practicals, the students will learn some of the skills to design, implement, and understand their own text mining pipeline. The topics in this course include preprocessing text, text classification, and word embedding.
The course deals with:
Participants should have a basic knowledge and motivation for scripting and programming in Python. It is recommended that participants attend Introduction to Python prior to this course.
Participants are requested to bring their own computer. Software will be available online.
Ayoub Bagheri
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 data science.
For an overview of all our Winter school courses offered by the Department of Methodology and Statistics please click here.
The course teaches participants the basic text mining techniques using Python. The skills addressed in this course are:
One day.
You will receive a certificate upon course completion. Please be aware that this course does not include graded activities, and therefore, we cannot provide a transcript of grades.
The tuition fee for staff off the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by FSBS.
PhD students from the Faculty of Social and Behavioural Sciences at Utrecht University have the opportunity to attend three Winter/Summer School courses funded by the Graduate School of Social and Behavioural Sciences. Additionally, they may choose to take as many courses as they wish at their own expense from their personal budget.
There are no scholarships available for this course.
