Sentiment Classification on Weibo Incidents Using CNN-SVM and Repost Tree
Manshu Tu, Shengxiang Gao, Zhe Ji, Yan Zhang, Yonghong Yan
Available Online December 2016.
- https://doi.org/10.2991/iceeecs-16.2016.7How to use a DOI?
- Sentiment classification. CNN-SVM. Repost tree
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 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 - https://doi.org/10.2991/iceeecs-16.2016.7 ID - Tu2016/12 ER -