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Text Analytics

In this course, you learn the essentials of text analytics.

About This Course

In this course, participants learn the essentials of Text Analytics. We start by outlining the conceptual foundations. A next chapter reviews some applications of Text Analytics from our own research. Next, we discuss various text preprocessing and representation methods to convert raw text documents into a numeric representation. We then zoom into various applications of text analytics such as visualisation, concept extraction, topic modelling, document clustering and text classification. The course concludes by illustrating how the concepts taught can be implemented in R. The subjects discussed in this course are based on our own experience of doing text analytics projects for more than 10 years now.

The course provides a sound mix of both theoretical and technical insights , as well as practical implementation details. These are illustrated by several real-life case studies and examples. Throughout the course, the instructors also extensively report upon their research and industry experience .

The course features more than 4 hours of video lectures, more than 50 multiple choice questions, and various references to background literature. A certificate signed by the instructors is provided upon successful completion.

Example real-life cases of Text Analytics in R are also provided and extensively discussed.

See Topic Modeling: Latent Semantic Analysis to get a free teaser of the course contents.

We can also come and teach this course on-site in classroom format. If interested, please mail us at:


The enrollment fee for this course is EUR 250 per participant. Payments are securely handled by PayPal but wire transfer is also possible if you give us your payment details (including VAT number!). See our about page to learn more about our mission statement.

After enrollment, participants will get 1 year unlimited access to all course material (videos, R/Python scripts, quizzes and certificate).


Before subscribing to this course, you should have a basic understanding of descriptive statistics (e.g., mean, median, standard deviation, histograms, scatter plots, etc.) and inference (e.g., confidence intervals, hypothesis testing). You should also have followed and completed our Machine Learning Essentials course.

Course Outline

  • Chapter 1: Conceptual Foundations
    • Setting the Stage
    • Creativity
    • Application creativity
    • Method creativity
    • Data creativity
    • Analyzing Text: Hurdles
    • Text Mining: Definition & Applications
  • Chapter 2: Application-based Examples
    • Customer Retention Management
    • Inferior Member Participation Prediction
    • Call Center Optimization
  • Chapter 3 Methodological Foundations
    • Key Definitions
    • Text Preprocessing
    • Raw Text Cleaning/Tokenization
    • Part-of-Speech Tagging
    • Term Filtering
    • Stemming/Lemmatization
    • Rule-based methods for Stemming
    • Statistical methods for Stemming
    • Text Representation
    • Syntactic Parsing
    • Vector-space approach
    • Bag-of-words
    • N-grams
    • Skip-grams
    • Word Embeddings
  • Chapter 4: Types of Text Analytics
    • Visualization
    • Concept Extraction
    • Dictionary-based Approaches
    • Topic Modeling
    • Latent Semantic Analysis
    • Probabilistic Latent Semantic Analysis
    • Latent Dirichlet Allocation
    • Document Clustering
    • Text Classification
  • Chapter 5: Text Analytics in R
    • Text Preprocessing in R
    • Text Representation in R
    • Text Analytics Applications in R

Course Staff

Prof. dr. Kristof Coussement

Prof. dr. Kristof Coussement

Kristof Coussement is born in Oudenaarde (East-Flanders, Belgium) on February, 12th 1982. He lives together with his lovely wife Ilse and his three soccer freaks (and sons!), Tobias, Oliver and Bastian. He grew up and still lives in the Walhalla of Belgian’s cycling scene with the Tour of Flanders being the most important cyclist event in Flanders. He regularly runs and plays golf as a 20 handicapper. He loves to debate and watch the most important sports events with friends around a box of Tripel Carmelite , i.e. his favorite beer.

Kristof Coussement is also professor of Business Analytics at the triple-crown accredited IÉSEG School of Management (France). He chairs a post-graduate MSc in Big Data Analytics for Business that is specifically designed to deliver business-value creating data scientists to the global job market. In his research, he primarily aims to advance the business analytics field by developing innovative, value creating decision support frameworks. He is acclaimed for his work on incorporating textual data sources into conventional – mainly predictive modeling – settings using text analytics and deep learning methodologies. Furthermore, he founded and chairs the IÉSEG School of Management Center for Marketing Analytics (ICMA) that is a research center focusing on developing innovation trajectories in data science with companies. He has been chair holder of several research projects for large European companies, including La Redoute (2014-2017), InSites Consulting (2014-2017), Leroy Merlin (2016-2019), and Crédit Agricole (2016-2019), and is currently leading the IÉSEG School of Management (2017-2021), Enfocus (2020-2023), Oney Bank (2020-2023) and Crédit Agricole (2020-2023) research chair. He serves as Senior Editor on the editorial board of Decision Support Systems. He regularly delivers tailor-made executive education workshops and advises companies on how to innovate and create business value through data science. He has solved various data science puzzles by crunching datasets originating the banking and insurance, retailing and e-tailing, entertainment and publishing, social media, IoT, human resources, consulting and telecommunication industry.

Prof. dr. Bart Baesens

Prof. dr. Bart Baesens

Bart was born in Bruges (West Flanders, Belgium) on February 27th, 1975. He speaks West-Flemish (which he is very proud of!), Dutch, French, a bit of German, some English and can order a beer in Chinese. He is married to Katrien Denys and has 3 kids (Ann-Sophie, Victor and Hannelore), and 2 cats (Felix and Simba). Besides enjoying time with his family, he is also a diehard Club Brugge soccer fan. Bart is a foodie and amateur cook. He loves drinking a good glass of wine (his favorites are white Viognier or red Cabernet Sauvignon) either in his wine cellar or when overlooking the authentic red English phone booth in his garden. His favourite pub is “In den Rozenkrans” in Kessel-Lo (close to Leuven) where you will often find him having a Gueuze Girardin 1882 or Tripel Karmeliet with a spaghetti of the house. Bart loves traveling and his favorite cities are: San Francisco, Sydney and Barcelona. He is fascinated by World War I and reads many books on the topic. He is not a big fan of being called professor Baesens (or even worse, professor Baessens), shopping (especially for clothes or shoes), pastis (or other anise-flavored drinks), vacuum cleaning (he can’t bare the sound), students chewing gum during their oral exam of Credit Risk Modeling (or had garlic for breakfast), long meetings (> 30 minutes), phone calls (asynchronous e-mail communication is a lot more efficient!), admin (e.g., forms and surveys) or French fries (Belgian fries are a lot better!). He is often praised for his sense of humor, although he is usually more modest about this. Bart is also a professor of Big Data and Analytics at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on Big Data & Analytics, Credit Risk Modeling, Fraud Detection and Marketing Analytics. He has written more than 250 scientific papers, some of which have been published in well-known international journals (e.g., MIS Quarterly, Machine Learning, Management Science, MIT Sloan Management Review and IEEE Transactions on Knowledge and Data Engineering) and presented at top international conferences (e.g., ICIS, KDD, CAISE). He has received various best paper and best speaker awards. Bart is the author of 8 books: Credit Risk Management: Basic Concepts (Oxford University Press, 2009), Analytics in a Big Data World (Wiley, 2014), Beginning Java Programming (Wiley, 2015), Fraud Analytics using Descriptive, Predictive and Social Network Techniques (Wiley, 2015), Credit Risk Analytics (Wiley, 2016), Profit Driven Business Analytics (Wiley, 2017), Web Scraping for Data Science with Python (Apress, 2018), and Principles of Database Management (Cambridge University Press, 2018). He sold more than 25.000 copies of these books worldwide, some of which have been translated in Chinese, Russian and Korean. His research is summarized at For an overview of the courses he is teaching, see He also regularly tutors, advises and provides consulting support to international firms regarding their big data, analytics and credit risk management strategy.