Abstract
Over the past few years, there has been a growing concern around toxic positivity on social media which is a phenomenon where positivity is used to minimize one’s emotional experience. In this paper, we create a dataset for toxic positivity classification from Twitter and an inspirational quote website. We then perform benchmarking experiments using various text classification models and show the suitability of these models for the task. We achieved a macro F1 score of 0.71 and a weighted F1 score of 0.85 by using an ensemble model. To the best of our knowledge, our dataset is the first such dataset created.- Anthology ID:
- 2022.socialnlp-1.7
- Volume:
- Proceedings of the Tenth International Workshop on Natural Language Processing for Social Media
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, Washington
- Editors:
- Lun-Wei Ku, Cheng-Te Li, Yu-Che Tsai, Wei-Yao Wang
- Venue:
- SocialNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 75–82
- Language:
- URL:
- https://aclanthology.org/2022.socialnlp-1.7
- DOI:
- 10.18653/v1/2022.socialnlp-1.7
- Cite (ACL):
- Ishan Sanjeev Upadhyay, KV Aditya Srivatsa, and Radhika Mamidi. 2022. Towards Toxic Positivity Detection. In Proceedings of the Tenth International Workshop on Natural Language Processing for Social Media, pages 75–82, Seattle, Washington. Association for Computational Linguistics.
- Cite (Informal):
- Towards Toxic Positivity Detection (Upadhyay et al., SocialNLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.socialnlp-1.7.pdf