Classic information extraction techniques consist in building questions and answers about the facts. Indeed, it is still a challenge to subjective information extraction systems to identify opinions and feelings in context. In sentiment-based NLP tasks, there are few resources to information extraction, above all offensive or hateful opinions in context. To fill this important gap, this short paper provides a new cross-lingual and contextual offensive lexicon, which consists of explicit and implicit offensive and swearing expressions of opinion, which were annotated in two different classes: context dependent and context-independent offensive. In addition, we provide markers to identify hate speech. Annotation approach was evaluated at the expression-level and achieves high human inter-annotator agreement. The provided offensive lexicon is available in Portuguese and English languages.
@article{arxiv.2104.12227,
title = {Identifying Offensive Expressions of Opinion in Context},
author = {Francielle Alves Vargas and Isabelle Carvalho and Fabiana Rodrigues de Góes},
journal= {arXiv preprint arXiv:2104.12227},
year = {2022}
}