Authors :
P.Shyam Kumar; K.Anirudh Reddy; G.Kritveek Reddy; V. lingamaiah
Volume/Issue :
Volume 8 - 2023, Issue 3 - March
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3FRquP0
DOI :
https://doi.org/10.5281/zenodo.7770950
Abstract :
The massive increase in online social
interaction activities such as social networking and
online gaming is frequently marred by hostile or
aggressive behavior, which can result in uninvited
manifestations of cyberbullying or harassment. In this
paper, we use self-attentive Convolutional Neural
Networks to build an audio-based toxic language
classifier (CNNs). Because definitions of hostility or
toxicity differ depending on the platform or application,
we take a more general approach to identifying toxic
utterances in this work, one that does not rely on
individual lexicon terms, but rather takes into account
the entire acoustical context of the short verse or
utterance. The self-attention mechanism in the proposed
architecture captures the temporal dependency of verbal
content by summarizing all relevant information from
different regions of the utterance. On a public and an
internal dataset, the proposed audio-based self-attentive
CNN model achieves 75% accuracy, 79% precision, and
80% recall in identifying toxicspeech recordings.
Keywords :
Toxic Language Detection, Self-Attention, Hate Speech, Sentiment Detection, Cyberbullying.
The massive increase in online social
interaction activities such as social networking and
online gaming is frequently marred by hostile or
aggressive behavior, which can result in uninvited
manifestations of cyberbullying or harassment. In this
paper, we use self-attentive Convolutional Neural
Networks to build an audio-based toxic language
classifier (CNNs). Because definitions of hostility or
toxicity differ depending on the platform or application,
we take a more general approach to identifying toxic
utterances in this work, one that does not rely on
individual lexicon terms, but rather takes into account
the entire acoustical context of the short verse or
utterance. The self-attention mechanism in the proposed
architecture captures the temporal dependency of verbal
content by summarizing all relevant information from
different regions of the utterance. On a public and an
internal dataset, the proposed audio-based self-attentive
CNN model achieves 75% accuracy, 79% precision, and
80% recall in identifying toxicspeech recordings.
Keywords :
Toxic Language Detection, Self-Attention, Hate Speech, Sentiment Detection, Cyberbullying.
