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An assessment of deep learning models and word embeddings for toxicity detection within online textual comments

Dessì, Danilo; Recupero, Diego Reforgiato; Sack, Harald

Abstract:
Today, increasing numbers of people are interacting online and a lot of textual comments are being produced due to the explosion of online communication. However, a paramount inconvenience within online environments is that comments that are shared within digital platforms can hide hazards, such as fake news, insults, harassment, and, more in general, comments that may hurt someone’s feelings. In this scenario, the detection of this kind of toxicity has an important role to moderate online communication. Deep learning technologies have recently delivered impressive performance within Natural Language Processing applications encompassing Sentiment Analysis and emotion detection across numerous datasets. Such models do not need any pre-defined hand-picked features, but they learn sophisticated features from the input datasets by themselves. In such a domain, word embeddings have been widely used as a way of representing words in Sentiment Analysis tasks, proving to be very effective. Therefore, in this paper, we investigated the use of deep learning and word embeddings to detect six different types of toxicity within online comments. In doing so, the most suitable deep learning layers and state-of-the-art word embeddings for identifying toxicity are evaluated. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000131133
Veröffentlicht am 08.04.2021
Originalveröffentlichung
DOI: 10.3390/electronics10070779
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2079-9292
KITopen-ID: 1000131133
Erschienen in Electronics (Switzerland)
Verlag MDPI
Band 10
Heft 7
Seiten 779
Schlagwörter deep learning; word embeddings; toxicity detection; binary classification
Nachgewiesen in Scopus
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