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, whic...

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Veröffentlicht in:2018 IEEE 4th International Conference on Computer and Communications (ICCC) S. 2312 - 2316
Hauptverfasser: Zhang, Liujie, Zhou, Yanquan, Chen, Ruiqi, Duan, Xiuyu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2018
Schlagworte:
Dictionaries
Feature extraction
Linear programming
multi-label
Semantics
Sentiment analysis
sentiment classification
sentiment-polarized
Task analysis
Training
word embedding
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Zusammenfassung: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, and our code is available in https://github.com/KillersDeath/SPWE.
DOI:10.1109/CompComm.2018.8780754