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 analyse, 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 pre-processing text, text classification, and word embedding.
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.
Participants should have a basic knowledge and a motivation of scripting and programming in Python.
Participants are requested to bring their own computer. Software will be available online.
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.
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.
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 participants the basic text mining techniques using Python. The skills addressed in this course are:
• Python environment
• Preprocessing text
• NLTK, Gensim, spaCy
• Text classification
• Sentiment classification
• Word embedding
• CBOW vs Skip-gram
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.
Utrecht Summer School does not offer scholarships for this course.
Irma Reyersen | E: email@example.com