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
These leaderboards are used to track progress in Aggression Identification
Most implemented papers
Aggressive Language Identification Using Word Embeddings and Sentiment Features
Several machine learning methods and different combinations of features were tried.
Aggression Identification Using Deep Learning and Data Augmentation
Social media platforms allow users to share and discuss their opinions online.
Bagging BERT Models for Robust Aggression Identification
In this paper, we describe such an ensemble system and present our submission to the shared tasks on aggression identification 2020 (team name: Julian).
Aggression Identification in English, Hindi and Bangla Text using BERT, RoBERTa and SVM
In our study, we used English BERT (En-BERT), RoBERTa, DistilRoBERTa, and SVM based classifiers for English language.
Aggression and Misogyny Detection using BERT: A Multi-Task Approach
In recent times, the focus of the NLP community has increased towards offensive language, aggression, and hate-speech detection. This paper presents our system for TRAC-2 shared task on {``}Aggression Identification{''} (sub-task A) and {``}Misogynistic Aggression Identification{''} (sub-task B).
Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection
There is content such as hate speech, offensive, toxic or aggressive documents, which are perceived differently by their consumers.