计算机技术与发展2018，Vol.28Issue(2)：88-93,6.DOI:10.3969/j.issn.1673-629X.2018.02.020
基于SVM的高维混合特征短文本情感分类
Short Text Sentiment Classification of High Dimensional Hybrid Feature Based on SVM
摘要
Abstract
Aiming at the characteristics of short texts which are sparse,unnormative and ambiguous in subject,we present a hybrid feature model with high dimension based on SVM.Firstly,we introduce six types of feature about both semantics and emotion,involving expression symbols,word clustering symbols,part-of-speech tagging,n-gram,negation and the sentiment dictionary,which are mainly introduced in their concept,extraction and output form.Then a five-fold crossover method is used to verify the validity of the model according to the data of COAE2014.The average accuracy rate is 84.69%,the average recall rate is 83.13%,and the average F1value is 83.90%.Thirdly,we discuss the influence of SVM regularization parameter on experiment.Finally,the proposed model is compared and analyzed with Recursive Auto Encoder,Doc2vec and so on,which show that it is more effective for short text emotion classification.关键词
情感分类/混合特征/支持向量机/情感词典Key words
sentiment classification/hybrid feature/SVM/emotion dictionary分类
信息技术与安全科学引用本文复制引用
王义真,郑啸,后盾,胡昊..基于SVM的高维混合特征短文本情感分类[J].计算机技术与发展,2018,28(2):88-93,6.基金项目
国家自然科学基金(61402008,61402009) （61402008,61402009）
安徽省科技重大专项(16030901060) （16030901060）
安徽省高校自然科学研究重大项目(KJ2014ZD05) （KJ2014ZD05）
安徽省高校优秀青年人才支持计划 （）
