research product . 2020

Multiklassificering af hadefulde ytringer med maskinlæring

Rasmussen, Emma; Hinnerskov, Joakim Hey; Sejsbo, Ask Harup; Kinch, Gustav Weber;
Open Access Danish
  • Published: 01 Jan 2020
  • Country: Denmark
Abstract
This paper revolves around the development of an LSTM multiclass classifier, constructed using Keras as framework and CRISP-DM as project process, with the purpose of classifying natural language into varying degrees of toxicity. The model takes a starting point in an existing toxic comment classification challenge from Kaggle.com, and makes a first iteration, engineered towards the requirements in the challenge. In this first iteration, several measures are taken to avoid common pitfalls of neural networks. The model is then held up against principles of freedom of speech including The Harm Principle and The Offence Principle by John Stuart Mill and Joel Feinbe...
Subjects
free text keywords: Natural Language Processing, LSTM, Hadefulde ytringer, Maskinlæring, Reccurent neural networks, Toxic comment classification
Related Organizations
Communities
Digital Humanities and Cultural Heritage
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