针对现有微博主客观分类方法特征冗余度高和未考虑特征选择方法之间的互补关系问题,该文提出了一种基于融合特征的微博主客观分类方法。通过对多种不同特征选择方法进行有效组合,利用特征融合算法对词特征、内容特征、微博特征等基本特征进行了选择和融合,以获取更加有效的主客观分类特征。在新浪微博数据上的实验结果表明,该特征融合算法能够获得比最优单一特征选择方法更好的分类效果。
Abstract
To deal with issues in the existing micro-blog subjective and objective classification such as high redundancy in features and failure in employing the complementarity among the feature selection method, this study proposes a feature fusion approach to subjective and objective classification of micro-blog. In order to get more effective features, the study combines a variety of different feature selection methods, and uses the feature fusion algorithm to select and fuse the basic features including word features, content features, micro-blog features and so on. The experimental results using Sina micro-blog data show that the feature fusion algorithm can achieve better performance than the best single one.
关键词
微博 /
主客观分类 /
特征选择 /
融合算法
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Key words
micro-blog /
subjective and objective classification /
feature selection /
fusion algorithm
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参考文献
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脚注
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基金
国家语委“十二五”科研规划项目(YB125-19);国家自然科学基金(61373082);国家自然科学基金(60970053); 山西省回国留学人员科研资助项目(2013-015);国家“863”高技术研究发展计划基金(2006AA0lZ142)
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