Greasing the Wheels for Comparative Communication Research: Supervised Text Classification for Multilingual Corpora

Autor(en)

Fabienne Lind, Tobias Heidenreich, Christoph Kralj, Hajo Boomgaarden

Abstrakt

Employing supervised machine learning for text classification is already a resource-intensive endeavor in a monolingual setting. However, facing the challenge to classify a multilingual corpus, the cost of producing the required annotated documents quickly exceeds even generous time and financial constraints. We show how tools like automated annotation and machine translation can not only efficiently but also effectively be employed for the classification of a multilingual corpus with supervised machine learning. Our findings demonstrate that good results can already be achieved with the machine translation of about 250 to 350 documents per category class and language and a dictionary in just one language, which we perceive as a realistic scenario for many projects. The methodological strategy is applied to study migration frames in seven languages (news discourse in seven European countries) and discussed and evaluated for its usability in comparative communication research.

Organisation(en)

Institut für Publizistik- und Kommunikationswissenschaft, Forschungsgruppe Visualization and Data Analysis

Journal

Computational Communication Research

Band

3

Seiten

1-30

Anzahl der Seiten

30

DOI

https://doi.org/10.5117/CCR2021.3.001.LIND

Publikationsdatum

10-2021

Peer-reviewed

Ja

ÖFOS 2012

508007 Kommunikationswissenschaft, 102019 Machine Learning

Schlagwörter

ASJC Scopus Sachgebiete

Computational Theory and Mathematics, Linguistics and Language

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/55aa30c7-4de9-4e08-9438-aeb304e60e65