Leveraging Sentiment for Offensive Text Classification
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Abstract (arXiv)
In this paper, we conduct experiment to analyze whether models can classify offensive texts better with the help of sentiment. We conduct this experiment on the SemEval 2019 task 6, OLID, dataset. First, we utilize pre-trained language models to predict the sentiment of each instance. Later we pick the model that achieved the best performance on the OLID test set, and train it on the augmented OLID set to analyze the performance. Results show that utilizing sentiment increases the overall performance of the model.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 30 references that are in the map. First posted on arXiv 9 Dec 2024.
Builds on
5 of 30 references.
References · 5
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- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
- 2019SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)cited by 207
- 2017Searching for Activation Functionscited by 169
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