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Multiklassificering af hadefulde ytringer med maskinlæring

Authors: Rasmussen, Emma; Hinnerskov, Joakim Hey; Sejsbo, Ask Harup; Kinch, Gustav Weber;

Multiklassificering af hadefulde ytringer med maskinlæring

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 Feinberg respectively. After evaluating upon the models performance in the light of these principles, a second iteration is constructed with some design changes. For reasons i.a. related to the dataset, this operation is less successful. The paper concludes that it is possible to make a good multiclassification tool for shallow NLP problem, but gets less efficient in later iterations as we try to apply it to more concrete purposes.

Country
Denmark
Related Organizations
Keywords

Maskinlæring, Hadefulde ytringer, Toxic comment classification, LSTM, Reccurent neural networks, Natural Language Processing

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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