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Aggressive language identification using word embeddings and sentiment features
This paper describes our participation in the First Shared Task on Aggression Identification. The
method proposed relies on machine learning to identify social media texts which contain aggression.
The main features employed by our method are information extracted from word embeddings
and the output of a sentiment analyser. Several machine learning methods and different
combinations of features were tried. The official submissions used Support Vector Machines and
Random Forests. The official evaluation showed that for texts similar to the ones in the training
dataset Random Forests work best, whilst for texts which are different SVMs are a better choice.
The evaluation also showed that despite its simplicity the method performs well when compared
with more elaborated methods
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