Julian Risch, Eva Krebs, Alexander Löser, Alexander Riese, Ralf Krestel
Our paper "Fine-Grained Classification of Offensive Language" has been accepted for presentation at the workshop of the Germeval Task 2018 — Shared Task on the Identification of Offensive Language, which is co-located with the Conference on Natural Language Processing / "Die Konferenz zur Verarbeitung natürlicher Sprache" (KONVENS). This system description paper is part of our comment analysis project and originated during our seminar on text mining in practice. The paper can be downloaded here.
Fine-Grained Classification of Offensive Language
Social media platforms receive massive amounts of user-generated content that may include offensive text messages. In the context of the GermEval task 2018, we propose an approach for fine-grained classification of offensive language. Our approach comprises a Naive Bayes classifier, a neural network, and a rule-based approach that categorize tweets. In addition, we combine the approaches in an ensemble to overcome weaknesses of the single models. We cross-validate our approaches with regard to macro-average F1-score on the provided training dataset.