Abstract
Rapid recognition of depression is an important step in the research of depression. With the development of social networking platform, more and more depressive patients regard micro-blog as one of the ways of self-expression. And this information provides support of data for the recognition of depression. In this study, the data crawled from micro-blog's “tree hole”[1] is used as experimental corpus. Combined with the features of micro-blog text with depression, a double-input convolutional neural network structure (D-CNN) is proposed. This method takes both the external features and the semantic features of text as input. By comparing the accuracy of classification with Support Vector Machine (SVM) and convolutional neural network (CNN) algorithm, it is finally shown that the D-CNN can further improve the accuracy of text classify.
Original language | English |
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Title of host publication | ACAI 2018 |
Subtitle of host publication | Proceeding of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence |
Publisher | Association for Computing Machinery |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Electronic) | 9781450366250 |
DOIs | |
Publication status | Published - Dec 2018 |
Event | 2018 International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2018 - Sanya, China Duration: 21 Dec 2018 → 23 Dec 2018 |
Conference
Conference | 2018 International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2018 |
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Country/Territory | China |
City | Sanya |
Period | 21/12/18 → 23/12/18 |
Keywords
- CNN
- D-CNN
- Micro-blog's “tree hole”
- Selection of features
- SVM
- Vector-matrix of sentences