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Found 6 papers, 1 papers with code
HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language
The detection of abusive or offensive remarks in social texts has received significant attention in research.
Do You Really Want to Hurt Me? Predicting Abusive Swearing in Social Media
In this study, we explore the phenomenon of swearing in Twitter conversations, taking the possibility of predicting the abusiveness of a swear word in a tweet context as the main investigation perspective.
Cross-domain and Cross-lingual Abusive Language Detection: A Hybrid Approach with Deep Learning and a Multilingual Lexicon
This makes abusive language detection a domain-dependent task, and building a robust system to detect general abusive content a first challenge.
Emotionally-Aware Chatbots: A Survey
We start with the history and evolution of EAC, then several approaches to build EAC by previous studies, and some available resources in building EAC.
Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure
On this line, a new shared task has been proposed at SemEval-2017 (Task 8, SubTask A), which is focused on rumour stance classification in English tweets.
\#NonDicevoSulSerio at SemEval-2018 Task 3: Exploiting Emojis and Affective Content for Irony Detection in English Tweets
This paper describes the participation of the {\#}NonDicevoSulSerio team at SemEval2018-Task3, which focused on Irony Detection in English Tweets and was articulated in two tasks addressing the identification of irony at different levels of granularity.
