Social Emotion Classification via Reader Perspective Weighted Model
Abstract
With the development of Web 2.0, many users express their opinions online. This paper is concerned with the classification of social emotions on varied-scale datasets. Different from traditional models which weight training documents equally, the concept of emotional entropy is proposed to estimate the weight and tackle the issue of noisy documents. The topic assignment is also used to distinguish different emotional senses of the same word. Experimental evaluations using different data sets validate the effectiveness of the proposed social emotion classification model.
Cite
Text
Li et al. "Social Emotion Classification via Reader Perspective Weighted Model." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.9922Markdown
[Li et al. "Social Emotion Classification via Reader Perspective Weighted Model." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/li2016aaai-social/) doi:10.1609/AAAI.V30I1.9922BibTeX
@inproceedings{li2016aaai-social,
title = {{Social Emotion Classification via Reader Perspective Weighted Model}},
author = {Li, Xin and Rao, Yanghui and Chen, Yanjia and Liu, Xuebo and Huang, Huan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {4230-4231},
doi = {10.1609/AAAI.V30I1.9922},
url = {https://mlanthology.org/aaai/2016/li2016aaai-social/}
}