Text classification of micro-blog's “tree hole” based on convolutional neural network

Xiaoli Zhao*, Shaofu Lin, Zhisheng Huang

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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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 languageEnglish
Title of host publicationACAI 2018
Subtitle of host publicationProceeding of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence
PublisherAssociation for Computing Machinery
Pages1-5
Number of pages5
ISBN (Electronic)9781450366250
DOIs
Publication statusPublished - Dec 2018
Event2018 International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2018 - Sanya, China
Duration: 21 Dec 201823 Dec 2018

Conference

Conference2018 International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2018
Country/TerritoryChina
CitySanya
Period21/12/1823/12/18

Keywords

  • CNN
  • D-CNN
  • Micro-blog's “tree hole”
  • Selection of features
  • SVM
  • Vector-matrix of sentences

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