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
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
527–533
Language:
URL:
https://www.aclweb.org/anthology/D17-1055
DOI:
10.18653/v1/D17-1055
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1055.pdf