Please use this identifier to cite or link to this item:

Title: Fully Connected Neural Network with Advance Preprocessor to Identify Aggression over Facebook and Twitter
Authors: Raiyani, Kashyap
Gonçalves, Teresa
Quaresma, Paulo
Nogueira, Vítor
Issue Date: 2018
Publisher: TRAC-2018 - ACL
Citation: Kashyap Raiyani, Teresa Gonçalves, Paulo Quaresma, and Vitor Beires Nogueira. Fully connected neural network with advance preprocessor to identify aggression over facebook and twitter. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbully- ing (TRAC-2018), pages 28–41. Association for Computational Linguistics, 2018.
Abstract: Aggression Identification and Hate Speech detection had become an essential part of cyberharassment and cyberbullying and an automatic aggression identification can lead to the interception of such trolling. Following the same idealization, vista.ue team participated in the workshop which included a shared task on ’Aggression Identification’. A dataset of 15,000 aggression-annotated Facebook Posts and Comments written in Hindi (in both Roman and Devanagari script) and English languages were made available and different classification models were designed. This paper presents a model that outperforms Facebook FastText (Joulin et al., 2016a) and deep learning models over this dataset. Especially, the English developed system, when used to classify Twitter text, outperforms all the shared task submitted systems.
Type: lecture
Appears in Collections:INF - Comunicações - Em Congressos Científicos Internacionais

Files in This Item:

File Description SizeFormat
document(9).pdf461.92 kBAdobe PDFView/Open
FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Dspace Dspace
DSpace Software, version 1.6.2 Copyright © 2002-2008 MIT and Hewlett-Packard - Feedback
UEvora B-On Curriculum DeGois