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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.17825 |
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| _version_ | 1866910761181249536 |
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| author | Islam, Khondoker Ittehadul |
| author_facet | Islam, Khondoker Ittehadul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17825 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging Sentiment for Offensive Text Classification Islam, Khondoker Ittehadul Computation and Language 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. |
| title | Leveraging Sentiment for Offensive Text Classification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.17825 |