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Author (up) Yingjie Li; Seoyeon Park; Cornelia Caragea; Doina Caragea; Andrea Tapia pdf  isbn
  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  
  Keywords Word Embedding, Deep Learning, Machine Learning, Sympathy Tweets Detection  
  Abstract Nowadays, micro-blogging sites such as Twitter have become powerful tools for communicating with others in

various situations. Especially in disaster events, these sites can be the best platforms for seeking or providing social

support, of which informational support and emotional support are the most important types. Sympathy, a sub-type

of emotional support, 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. Specifically, we

propose a refined word embedding technique in terms of various pre-trained word vector models and show great

performance of CNNs that use these refined embeddings in the sympathy tweet classification task. We also report

experimental results showing that the CNNs with the refined word embeddings outperform not only traditional

machine learning techniques, such as Naïve Bayes, Support Vector Machines and AdaBoost with conventional

feature sets as bags of words, but also Long Short-Term Memory Networks.
  Address University of Illinois at Chicago, United States of America;Kansas State University, United States of America;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 English 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 Approved no  
  Call Number Serial 1899  
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