摘要： 基于向量空间模型、潜在语义分析等传统文本意见分类模型将文本映射到词汇或语义空间中,侧重于词汇
的辨别能力,无法对映像空间给出明确的语义说明,导致其扩展性、准确率等方面的性能受到限制。为此,在人类
情感分类理论的基础上,假设文本中的意见表达与人们的情感存在较强的关联,结合词汇语义扩展、特征选择等方
法构造3 种情感表示模型,把表达人类情感倾向的文本转换到情感空间中,利用情感模型对国外股票论坛信息提
取情感特征,构建情感模型,并设计文本意见分类方法。针对实际股票论坛的数据进行实验,结果表明,该分类方
法能获得较高的分类准确率。
关键词:
Ekman 模型,
意见分类,
特征选择,
情感模型,
机器学习
Abstract: Abstract Traditional text classification models and latent semantic analysis model map text to vocabulary text
or semantic space,focusing on the ability to distinguish words. But it can not give a clear image of semantic description of the space. As a result,the scalability and accuracy of a text classification algorithm is limited. In this paper,based on the classification of human emotions in psychology,it assumes that there is a strong association between emotions and opinions. It uses lexical semantic extension and feature selection methods to build three emotional representation model, and maps documents which can express human emotions tended to the emotional space. Using emotion features in stock message board obtained by the emotional representation model,it builds the emotion space model and designs opinion classification method. Experimental results on actual stock forum show that the classification accuracy of this method is high.
Key words:
Ekman model,
opinion classification,
feature selection,
emotion model,
machine learning
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