TY - GEN
T1 - Identifying and tracking sentiments and topics from social media texts during natural disasters
AU - Yang, Min
AU - Mei, Jincheng
AU - Ji, Heng
AU - Zhao, Wei
AU - Zhao, Zhou
AU - Chen, Xiaojun
N1 - Publisher Copyright:
© 2017 Association for Computational Linguistics.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85063083298&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063083298&partnerID=8YFLogxK
U2 - 10.18653/v1/d17-1055
DO - 10.18653/v1/d17-1055
M3 - Conference contribution
AN - SCOPUS:85063083298
T3 - EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
SP - 527
EP - 533
BT - EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
PB - Association for Computational Linguistics (ACL)
T2 - 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017
Y2 - 9 September 2017 through 11 September 2017
ER -