一种基于情感句模的文本情感分类方法

考虑到同类型的情感句往往具有相同或者相似的句法和语义表达模式,该文提出了一种基于情感句模的文本情感自动分类方法。首先,将情感表达相关句模人工分为3大类105个二级分类;然后,设计了一种利用依存特征、句法特征和同义词特征的句模获取方法,从标注情感句中半自动地获取情感句模。最后,通过对输入句进行情感句模分类实现文本情感分类。在NLP&CC2013中文微博情绪分类评测语料及RenCECps博客语料的实验结果显示,该文提出的分类方法准确率显著高于基于词特征支持向量机分类器。

A Sentiment Classification Approach Based on Sentiment Sentence Framework

Considering that opinionated sentences always have the same or similar syntax and semantic expression frameworks, this paper proposes a sentiment analysis approach based on sentiment sentence framework. Firstly, we divided sentiment sentence frameworks into three categories and 105 subcategories. A sentence framework extraction method is designed to semi-automatically extract sentiment sentence frameworks from annotated sentiment sentences using dependency features, syntactic features and synonym features. The polarity of input sentence is determined through the classification of its sentiment sentence frameworks. The evaluations on NLP&CC 2013 micro-blog emotion analysis corpus and RenCECps blog emotion corpus show that our proposed sentiment classification approach achieves better precision performance compared to word-based support vector machine classifiers.
Key wordssentence framework; sentiment classification; syntactic feature; dependency feature