This paper is in the following e-collection/theme issue:

Natural Language Processing (706) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1666) Infodemiology and Infoveillance (1369) New Methods (963) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050) Methods in Cancer Research (31) Machine Learning (1501)

Published on in Vol 5, No 3 (2017): Jul-Sept

What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer

What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer

What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer

Authors of this article:

Mike Donald Tapi Nzali1, 2 Author Orcid Image ;   Sandra Bringay2, 3 Author Orcid Image ;   Christian Lavergne1, 3 Author Orcid Image ;   Caroline Mollevi4 Author Orcid Image ;   Thomas Opitz5 Author Orcid Image
Article Authors Cited by (53) Tweetations (10) Metrics

Mike Donald Tapi Nzali   1, 2 , PhD ;   Sandra Bringay   2, 3 , PhD ;   Christian Lavergne   1, 3 , PhD ;   Caroline Mollevi   4 , PhD ;   Thomas Opitz   5 , PhD

1 Institut Montpelliérain Alexander Grothendieck (IMAG), Department of Mathematics, Montpellier University, Montpellier, France

2 Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM), Department of Computer Science, Montpellier University, Montpellier, France

3 Paul Valery University, Montpellier, France

4 Biometrics Unit, Institut du Cancer Montpellier (ICM), Montpellier, France

5 BioSP Unit, Institut National de la Recherche Agronomique (INRA), Avignon, France

Corresponding Author:

  • Mike Donald Tapi Nzali, PhD
  • Institut Montpelliérain Alexander Grothendieck (IMAG)
  • Department of Mathematics
  • Montpellier University
  • Place Eugène Bataillon
  • Case Courrier 051
  • Montpellier, 34095
  • France
  • Phone: 33 4 67 41 86 72
  • Fax: 33 4 67 41 85 00
  • Email: tapinzali@lirmm.fr