@InProceedings{yang-EtAl:2017:EMNLP20171,
  author    = {Yang, Min  and  Mei, Jincheng  and  Ji, Heng  and  wei, zhao  and  Zhao, Zhou  and  Chen, Xiaojun},
  title     = {Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {527--533},
  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.
	Author{5}{Affiliation}},
  url       = {https://www.aclweb.org/anthology/D17-1055}
}