Multi-modal hate speech detection using machine learning
Abstract
Hate speech is a common problem that people face in any content based applications.
With continuous growth of internet users and media contents, it is very hard to track
down hateful speech in audio and video. Converting video or audio into text does
not detect hate speech accurately as humans sometimes use not hateful words as
hate speech in a sarcastic way and also uses di erent voice tone or shows di erent
action in the video than text. In the research, a combined approach to detect hate
speech from contents using video, audio and speech by extracting feature images,
feature values extracted from audio, text and used Machine learning, Deep learning
and Natural language processing to detect hate speech.
Keywords
Audio hate speech; Video hate speech; Hate speech detection; Multi-modal hate speech detectionDescription
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.Department
Department of Computer Science and Engineering, Brac UniversityType
ThesisCollections
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