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Author (up) Yingjie Li; Seoyeon Park; Cornelia Caragea; Doina Caragea; Andrea Tapia pdf 
  Title Sympathy Detection in Disaster Twitter Data Type Conference Article
  Year 2019 Publication Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2019  
  Volume Issue Pages 788-798  
  Keywords Word Embedding; Deep Learning; Machine Learning; Sympathy Tweets Detection  
  Abstract Sympathy is an expression of one's compassion or sorrow for a difficult situation that another person is facing. Providing sympathy to people affected by a disaster can help change people's emotional states from negative to positive emotions, and hence, help them feel better. Moreover, detecting sympathy contents in Twitter can potentially be used for finding candidate donors since the emotion ``sympathy'' is closely related to people who may be willing to donate. Thus, in this paper, as a starting point, we focus on detecting sympathy-related tweets. We address this task using Convolutional Neural Networks (CNNs) with refined word embeddings. We also report experimental results showing that the CNNs with the refined word embeddings outperform not only Long Short Term Memory Networks, but also traditional machine learning techniques, such as Naive Bayes, Support Vector Machines and AdaBoost with conventional feature sets as bags of words.  
  Address (1) University of Illinois at Chicago, United States of America; (2) Kansas State University, United States of America; (3) Pennsylvania State University, United States of America  
  Corporate Author Thesis  
  Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-84-09-10498-7 Medium  
  Track T8- Social Media in Crises and Conflicts Expedition Conference 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)  
  Notes yli300@uic.edu; spark313@uic.edu; cornelia@uic.edu; dcaragea@ksu.edu; atapia@psu.edu Approved no  
  Call Number Serial 1761  
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