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Text Mining

  • Teacher(s):   M.Boldi  
  • Course given in: English
  • ECTS Credits: 6 credits
  • Schedule: Autumn Semester 2020-2021, 4.0h. course (weekly average)
  •  sessions
  • site web du cours course website
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Objectives

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.

Contents

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.

References

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

Pre-requisites

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. Likewise, good knowledge in R programing are needed to succeed for this class.

Evaluation

First attempt

Exam:
Without exam (cf. terms)  
Evaluation:

- 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 = 0.3 exam + 0.7 project

Retake

Exam:
Written 2h00 hours
Documentation:
Not allowed
Calculator:
Not allowed
Evaluation:

The retake is organized on the parts that were failed (written exam or/and project).

- Written exam: a retake exam of 2hrs will be organized.

- Project: a complement/correction to the project will be required.

Only the retaking grade will be concerned

Final grade = 0.3 exam + 0.7 project

Examples:

- Final grade >= 4, no retake

- Final grade < 4

- with exam > 4, project < 4 => retake on the project, exam grade is kept

- with exam < 4, project >= 4 => retake on the exam, project grade is kept

- with both exam and project < 4 => retake on both



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