Abstract
The reported work is our straightforward approach for the shared task “Classification of tweets self-reporting age” organized by the “Social Media Mining for Health Applications (SMM4H)” workshop. This literature describes the approach that was used to build a binary classification system, that classifies the tweets related to birthday posts into two classes namely, exact age(positive class) and non-exact age(negative class). We made two submissions with variations in the preprocessing of text which yielded F1 scores of 0.80 and 0.81 when evaluated by the organizers.- Anthology ID:
- 2022.smm4h-1.12
- Volume:
- Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Graciela Gonzalez-Hernandez, Davy Weissenbacher
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 42–43
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.12
- DOI:
- Cite (ACL):
- Keshav Kapur, Rajitha Harikrishnan, and Sanjay Singh. 2022. MaNLP@SMM4H’22: BERT for Classification of Twitter Posts. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 42–43, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- MaNLP@SMM4H’22: BERT for Classification of Twitter Posts (Kapur et al., SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.smm4h-1.12.pdf