| |
Record |
Links |
|
Author  |
Yingjie Li; Seoyeon Park; Cornelia Caragea; Doina Caragea; Andrea Tapia |
|
| |
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 |
|
| Share this record to Facebook |
