Master Thesis Online
Mining entrepreneurs' profiles from tweets
Gémis, Guillaume 
Promotor(s) :
Ittoo, Ashwin
Date of defense : 2-Sep-2020/8-Sep-2020 • Permalink : http://hdl.handle.net/2268.2/10610
Details
| Title : | Mining entrepreneurs' profiles from tweets |
| Translated title : | [fr] Exploration du profil d'entrepreneur grâce à leurs tweets |
| Author : | Gémis, Guillaume
|
| Date of defense : | 2-Sep-2020/8-Sep-2020 |
| Advisor(s) : | Ittoo, Ashwin
|
| Committee's member(s) : | Ooms, Frédéric
Poumay, Judicaël
|
| Language : | English |
| Number of pages : | 108 |
| Keywords : | [en] Entrepreneurship [en] Machine learning [en] Ambidexterity [en] Start-ups [en] Established company [en] Text classification [en] Topic modelling [en] Word embedding |
| Discipline(s) : | Business & economic sciences > Multidisciplinary, general & others |
| Target public : | Researchers Professionals of domain Student General public |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Degree: | Master en ingénieur de gestion, à finalité spécialisée en Supply Chain Management and Business Analytics |
| Faculty: | Master thesis of the HEC-Ecole de gestion de l'Université de Liège |
Abstract
[en] New waves of discoveries and research in various fields have been possible thanks to machine learning. These algorithm-based learning methods allow the identification of information and trends from a large amount of data. Entrepreneurship can also benefit from this impetus to deepen its theories and knowledge about the path an entrepreneur follows. The aim of this dissertation is to participate in this movement on entrepreneurship using these new methods for the first time to profile entrepreneurs from their tweets.
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The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.