Please use this identifier to cite or link to this item:
http://hdl.handle.net/10174/24423
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| 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. |
| URI: | http://aclweb.org/anthology/W18-4404
http://hdl.handle.net/10174/24423 |
| Type: | lecture |
| Appears in Collections: | INF - Comunicações - Em Congressos Científicos Internacionais
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