https://github.com/aurelius84/spwe

Sentiment-Polarized Word Embedding for Multi-Label Sentiment Classification
https://github.com/aurelius84/spwe

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Sentiment-Polarized Word Embedding for Multi-Label Sentiment Classification

• Host: GitHub
• URL: https://github.com/aurelius84/spwe
• Owner: Aurelius84
• License: mit
• Created: 2018-06-29T17:21:15.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-10-07T15:38:16.000Z (about 7 years ago)
• Last Synced: 2025-03-24T05:51:59.118Z (7 months ago)
• Language: Python
• Size: 5.03 MB
• Stars: 12
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Sentiment-Polarized Word Embedding for Multi-Label Sentiment Classification

Sentiment analysis of text is an import branch of natural language process. In this paper, we propose a sentiment-polarized word embedding model (SPWE) with emotional dictionary, which is a variant of the C&W. Our model is able to represent and differentiate the emotional semantic of words, which is critical in sentiment classification tasks. This weak supervised model can perform large scale corpus training just with an open source emotional dictionary. In addition, we optimize the objective function from our previous hierarchical model and propose a Relax Margin loss function. The result shows it can reduce overfitting and improve generalization ability by restricting approximation degree to probability distribution of the multi-labels. We release our manually labeled comment dataset with multi tags from our previous work.

@inproceedings{liujiezhangbupt@gmail.com,

  title={Sentiment-Polarized Word Embedding for Multi-Label Sentiment Classification},

  

  author={Liujie Zhang, Yanquan Zhou and Xiuyu Duan, Ruiqi Chen},

  

  booktitle={ICCC},

  

  year={2018}

  

}