Multi‐feature‐Based Subjective‐Sentence Classification Method for Chinese Micro‐blogs
| Autor: | Yuru Jiang, Gaijuan Huang, Yangsen Zhang, Yaorong Zhang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Dependency (UML)
Computer science business.industry Applied Mathematics Feature extraction Sentiment analysis 020206 networking & telecommunications Pattern recognition 02 engineering and technology Part of speech Support vector machine Set (abstract data type) ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business Sentence |
| Zdroj: | Chinese Journal of Electronics. 26:1111-1117 |
| ISSN: | 2075-5597 1022-4653 |
| DOI: | 10.1049/cje.2017.09.006 |
| Popis: | The accurate classification of subjective and objective sentences is important in the preparation for micro-blog sentiment analysis. Since a single feature type cannot provide enough subjective information for classification, we propose a Support vector machine (SVM)-based classification model for Chinese micro-blogs using multiple features. We extracted the subjective features from the Part of speech (POS) and the dependency relationship between words, and constructed a 3-POS subjective pattern set and a dependency template set. We fused these two types of features and used an SVM-based model to classify Chinese micro-blog text. The experimental results showed that the performance of the classification model improved remarkably when using multiple features. |
| Databáze: | OpenAIRE |
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