Aggression Identification
6 papers with code • 0 benchmarks • 2 datasets
Develop a classifier that could make a 3-way classification in-between ‘Overtly Aggressive’, ‘Covertly Aggressive’ and ‘Non-aggressive’ text data. For this, TRAC-2 dataset of 5,000 aggression-annotated data from social media each in Bangla (in both Roman and Bangla script), Hindi (in both Roman and Devanagari script) and English for training and validation is to be used.
Benchmarks
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Latest papers with no code
IRIT at TRAC 2018
This paper describes the participation of the IRIT team to the TRAC 2018 shared task on Aggression Identification and more precisely to the shared task in English language.
Fully Connected Neural Network with Advance Preprocessor to Identify Aggression over Facebook and Twitter
Paper presents the different methodologies developed {\&} tested and discusses their results, with the goal of identifying the best possible method for the aggression identification problem in social media.
TRAC-1 Shared Task on Aggression Identification: IIT(ISM)@COLING'18
This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards TRAC-1 Shared Task on Aggression Identification in Social Media for COLING 2018.
An Ensemble Approach for Aggression Identification in English and Hindi Text
This paper describes our system submitted in the shared task at COLING 2018 TRAC-1: Aggression Identification.
Aggression Identification and Multi Lingual Word Embeddings
The system presented here took part in the 2018 Trolling, Aggression and Cyberbullying shared task (Forest and Trees team) and uses a Gated Recurrent Neural Network architecture (Cho et al., 2014) in an attempt to assess whether combining pre-trained English and Hindi fastText (Mikolov et al., 2018) word embeddings as a representation of the sequence input would improve classification performance.
Merging Datasets for Aggressive Text Identification
Regarding these, we merged two datasets, and the results showed that training with similar data is an advantage in the classification of social networks data.
Filtering Aggression from the Multilingual Social Media Feed
Using the validation data, we found that validation accuracy of our deep learning models outperform all standard machine learning classifiers and voting based ensemble techniques and results on test data support these findings.
RiTUAL-UH at TRAC 2018 Shared Task: Aggression Identification
This paper presents our system for "TRAC 2018 Shared Task on Aggression Identification".
LSTMs with Attention for Aggression Detection
In this paper, we describe the system submitted for the shared task on Aggression Identification in Facebook posts and comments by the team Nishnik.
