Abstract
This paper describes our approach to Task 3 of the Social Media Mining for Health 2024 (SMM4H’24) shared tasks. The objective of the task was to classify the sentiment of social media posts, taken from the social anxiety subreddit, with reference to the outdoors, as positive, negative, neutral, or unrelated. We classified posts using a relevance-weighted sentiment analysis, which scored poorly, at 0.45 accuracy on the test set and 0.396 accuracy on the evaluation set. We consider what factors contributed to these low scores, and what alternatives could yield improvements, namely: improved data cleaning, a sentiment analyzer trained on a more suitable data set, improved sentiment heuristics, and a more involved relevance-weighting.- Anthology ID:
- 2024.smm4h-1.22
- Volume:
- Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Dongfang Xu, Graciela Gonzalez-Hernandez
- Venues:
- SMM4H | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 98–100
- Language:
- URL:
- https://aclanthology.org/2024.smm4h-1.22
- DOI:
- Cite (ACL):
- Jessica Elliott and Roland Elliott. 2024. interrupt-driven@SMM4H’24: Relevance-weighted Sentiment Analysis of Reddit Posts. In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 98–100, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- interrupt-driven@SMM4H’24: Relevance-weighted Sentiment Analysis of Reddit Posts (Elliott & Elliott, SMM4H-WS 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.smm4h-1.22.pdf