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Indonesia Hate Speech Detection Using Deep Learning
Sutejo T.L.a, Lestari D.P.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 IEEE.Hate speech brings negative impacts not only to the target victim, but also to the listener. The spread of hate speech can be done not only through the social media postings, but also through the video, campaign or speech. In this research, we develop models to detect hate speech in Indonesian Language from input text and speech by using deep learning approach. We utilized both textual and acoustic features and compare their accuracies. Experiments result showed that hate speech detection using only textual features is better than that of using acoustic features and both of combined features model. The best model using textual feature obtained Fl-score 87.98% which is higher than the model of using acoustic feature only (Fl-score 82.5%), and the model of using acoustic and lexical features (Fl-score 86.98%).[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]accoustic features,Acoustic features,Combined features,Indonesian languages,Learning approach,Multi features,Speech detection,Textual features[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]accoustic features,deep learning,hate speech,Indonesian Language,multi-features,textual features[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This research is partially funded by PENELITIAN TERAPAN UNGGULAN PERGURUAN TINGGI program with title ‘Sistem Cerdas Pemantau Perilaku Penggunaan Gadget di Kalangan Remaja Menggunakan Teknik Pembelajaran Mesin’ (intelligent system for monitoring gadget usage behavior among teenagers using machine learning). We also thank PT. Prosa Solusi Cerdas and other parties who contributed in this experiment or during the process of working for this paper.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/IALP.2018.8629154[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]