Ce cours apparaît dans les formations suivantes :
• IÉSEG > MSc in Big Data Analytics for Business > Semester 2 > 2,00 ECTS

Pré requis

Basic knowledge of programming concepts, preferably in R; basic concepts of statistics

Objectifs du cours

At the end of the course, the student should be able to:
- use basic concepts of text mining such as text preprocessing, frequency distributions and classification
- apply the concepts to international and multilingual contexts
- connect to (social media) APIs and extract data
- extract company-relevant information from text and social media in particular, e.g. to evaluate general sentiment about a brand, understanding of the values of the organization spread in social media and effectiveness of customer care
- work together in groups to set up a streamlined approach to collect and process social media data, with feedback loops within the groups and peer-to-peer evaluation
- report the results of the analysis in managerial presentation in fluent English

These competencies and/or skills contribute to the following learning objectives
- 1.A Demonstrate an international mindset
- 1.C Communicate effectively in English
- 2.A Assess the values of the organization in which they work
- 4.B Compose constructive personal feedback and guidance

Contenu du cours

The course starts with a general presentation of text mining concepts, followed by a tutorial in which the students can apply the learnings. We will touch on more advanced text mining concepts. The course will discuss the usage of APIs for social media analytics, and how to set this up from within R. The second part of the course will be devoted to group projects focussing on real-world scenarios based on social network data. The projects will be prototyped during the course hours and finalised in personal work mode.

Modalités d'enseignement

Organisation du cours

Méthodes pédagogiques

• Coaching
• Presentation
• Project work
• Tutorial

Évaluation

The final grade will be determined by the in-class participation of the students as well as by the quality of the group projects in which they will be participating in the second part of the course and as part of their personal work. Interim project status will be presented during the class; the presentation will also be included into the final grade. Finally, there will be a small final exam to test knowledge of fundamental concepts.

Ressources internet

• Documentation of tm package (R)

• Basic text mining in R