Multi-feature-Based Subjective-Sentence Classification Method for Chinese Micro-blogs |
| |
Affiliation: | Beijing Information and Technology University, Beijing 100192, China |
| |
Abstract: | 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. |
| |
Keywords: | Micro-blog sentiment analysis Subjective and objective classification Support vector machine (SVM) 3-POS subjective pattern set Dependency template set |
本文献已被 万方数据 等数据库收录! |