Sentiment Classification on Weibo Incidents Using CNN-SVM and Repost Tree

DOI

10.2991/iceeecs-16.2016.7How to use a DOI?

Keywords

Sentiment classification. CNN-SVM. Repost tree

Abstract

Sentiment classification on weibo has recently attracted wide attention in research community. Most previous works are focused on weibo comments regarding movies or products. Our study, in contrast, is aimed at gusty incidents on weibo. Comments of the incidents are considered either positive or negative representing attitudes of users towards these incidents. Classifying users' attitudes helps identifying the general opinion of the public. In this paper, we propose an innovative convolutional neural networks based method, termed as CNN-SVM, to classify the incident comments. In addition, according to users' repost actions, we propose a new data structure, repost tree, for dealing with ambiguity in the comments. Extensive experiments demonstrate that the CNN-SVMmethod effectively improves the accuracy of incidents sentiment classification. The new data structure shows to be effective on steering the classification results towards real world sentiment tendency.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Manshu Tu
AU  - Shengxiang Gao
AU  - Zhe Ji
AU  - Yan Zhang
AU  - Yonghong Yan
PY  - 2016/12
DA  - 2016/12
TI  - Sentiment Classification on Weibo Incidents Using CNN-SVM and Repost Tree
BT  - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
PB  - Atlantis Press
SP  - 26
EP  - 29
SN  - 2352-538X
UR  - https://doi.org/10.2991/iceeecs-16.2016.7
DO  - 10.2991/iceeecs-16.2016.7
ID  - Tu2016/12
ER  -