Home | << 1 >> |
Record | |||||
---|---|---|---|---|---|
Author | Venkata Kishore Neppalli; Cornelia Caragea; Doina Caragea | ||||
Title | Deep Neural Networks versus Naive Bayes Classifiers for Identifying Informative Tweets during Disasters | Type | Conference Article | ||
Year | 2018 | Publication | ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2018 |
Volume | Issue | Pages | 677-686 | ||
Keywords | deep neural networks, naive bayes classifiers, handcrafted features | ||||
Abstract | In this paper, we focus on understanding the effectiveness of deep neural networks by comparison with the effectiveness of standard classifiers that use carefully engineered features. Specifically, we design various feature sets (based on tweet content, user details and polarity clues) and use these feature sets individually or in various combinations, with Naïve Bayes classifiers. Furthermore, we develop neural models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with handcrafted architectures. We compare the two types of approaches in the context of identifying informative tweets posted during disasters, and show that the deep neural networks, in particular the CNN networks, are more effective for the task considered. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Rochester Institute of Technology | Place of Publication | Rochester, NY (USA) | Editor | Kees Boersma; Brian Tomaszeski |
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 2411-3387 | ISBN | 978-0-692-12760-5 | Medium | |
Track | Social Media Studies CO - | Expedition | Conference | ||
Notes | Approved | no | |||
Call Number | Serial | 2141 | |||
Share this record to Facebook |