Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters
Min Yang, Jincheng Mei, Heng Ji, Wei Zhao, Zhou Zhao, Xiaojun Chen
Abstract
We study the problem of identifying the topics and sentiments and tracking their shifts from social media texts in different geographical regions during emergencies and disasters. We propose a location-based dynamic sentiment-topic model (LDST) which can jointly model topic, sentiment, time and Geolocation information. The experimental results demonstrate that LDST performs very well at discovering topics and sentiments from social media and tracking their shifts in different geographical regions during emergencies and disasters. We will release the data and source code after this work is published.- Anthology ID:
- D17-1055
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 527–533
- Language:
- URL:
- https://aclanthology.org/D17-1055
- DOI:
- 10.18653/v1/D17-1055
- Cite (ACL):
- Min Yang, Jincheng Mei, Heng Ji, Wei Zhao, Zhou Zhao, and Xiaojun Chen. 2017. Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 527–533, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters (Yang et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/D17-1055.pdf