摘要
为了对社交网络平台上发表的言论和信息进行情感分类,基于卷积神经网络和多特征融合,提出一种情感分类方法。结合Twitter自身语言特性和情感字典资源设计语料特征和词典特征,对Twitter文本词向量使用卷积神经网络获得对应的深度词向量特征,将上述3类特征进行特征融合并采用One-Versus-One SVM实现情感极性的分类判别。针对SemEval语料的实验结果表明,该方法取得了较好的情感分类效果,多特征融合能够有效地提高情感分类的准确性。
In order to classify the emotion for users expressions and comments on social networks,this paper presents a sentiment classification method which combines Convolutional Neural Network（CNN） and multi-feature fusion.It designs corpus features and lexicon features according to the characteristics of Twitter texts and semantic lexicons,uses the convolution neural network for the word vector of Twitter text to get the depth word vector features,combines the above three features to construct the feature fusion and uses One-Versus-One SVM to obtain the sentiment polarity classification and discrimination.Experimental results on SemEval corpus show this method performs a good result and the multi-feature fusion can efficiently improve the accuracy of sentiment classification.
出处
《计算机工程》
CAS
CSCD
北大核心
2018年第2期210-219,共10页
Computer Engineering
基金
国家自然科学基金(61373108)
关键词
文本分类
情感分析
卷积神经网络
词向量
特征融合
text classification
sentiment analysis
Convolutional Neural Network（CNN）
word vector
feature fusion
