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Text Mining

  • Teacher(s):   M.Boldi  
  • Course given in: English
  • ECTS Credits: 6 credits
  • Schedule: Autumn Semester 2019-2020, 4.0h. course (weekly average)
  •  sessions
  • site web du cours course website
  • Related programmes:
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    Maîtrise universitaire ès Sciences en management, Orientation Behaviour, Economics and Evolution

    Master of Science (MSc) in Management, Orientation Business Analytics



Upon completion of the course, the student will be able to

  • Select and apply rigorous text mining methods to the cases covered during the course,
  • Use R to carry out these applications,
  • Analyze and interpret text mining method results.


This course presents several text mining techniques in a business context. The tentative list of topics below will be adapted according to the pace of the class:

  • Document acquisition (web scraping, encoding, ...) and pre-treatment (stemming, lemmatizing, ...),
  • Frequencies and word embedding (TF-IDF, word2vec, ...),
  • Unsupervised learning (similarities, latent semantic analysis, topic modeling, ...),
  • Supervised learning.

Theory and practice are equally important.


No mandatory readings. Useful references will be communicated during the course.


No mandatory prerequisites but the course extensively uses machine learning techniques. The students are supposed to know these methods (to the same level as the corresponding course in the master). If not, they are supposed to learn them on their own before the class.


First attempt

Without exam (cf. terms)  

- One individual written exam: 2hrs, organized during the semester.

- One applied project: individual or in groups, depending on the number of participants to the course. A final presentation will be organized during the semester.

Final grade = 0.4 exam + 0.6 project


Oral 0h15 minutes
Allowed with restrictions

One oral exam (100% final grade).

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