基于三支决策的多粒度文本情感分类模型

摘要

文本情感分类是一项重要的自然语言处理任务,具有广泛的应用场景.以往的情感分类方法过于注重分类准确率,忽略了训练和分类过程的时间代价,而且使用的特征大多为词袋特征,存在维度高、可解释性差的缺点.针对这些问题,将粒计算的思想运用于文本数据的三层粒度结构(词-句-篇章),提出一种具有强可解释性的文本情感分类特征——SSS(Sentence-level Sentiment Strength)特征,SSS特征每一维度代表文章中每个句子的情感强度值;同时,在分类过程中,利用三支决策方法将待分类对象划分为3个区域,位于正域和负域的对象直接划分至正类和负类中,使用SVM(Support Vector Machine) +SSS特征对位于边界域的对象做进一步分类.实验结果显示,SSS特征由于自身的低维特性,能够大大降低特征提取和模型训练过程所耗费的时间成本,结合了三支决策方法的SVM能够进一步提高分类准确率,而且三支决策方法可以减少分类过程所耗费的时间.%Text sentiment classification is a very important branch of natural language processing.Researchers focus on the accuracy of sentiment classification but ignore the time cost of training and classification.Bag-of-words feature used in most methods for text sentiment classification has high dimension and bad interpretability.To solve the above problems,we presented a multi-granularity text sentiment classification model based on three-way decisions for documentlevel sentiment classification.With the aid of granular computing,we made a structure of text that contains three levels of granularity-word,sentence and document,and presented a new kind of feature-SSS(sentence-level sentiment strength) feature which represents a document,in which the value of each dimension is the sentence-level sentiment strength.In classification process,we firstly utilized three-way decisions method to divide the objects into three regions.The objects in positive region and negative region are classified into positive class and negative class,respectively.We employed the state-of-the-art classifier-SVM to classify the objects in boundary region.Experimental results show that combining three way decisions method and SVM can improve the accuracy of classification.The SSS-feature reduces the time-cost of feature extraction and training greatly because of its low dimension.Three-way decisions method can reduce the time-cost of classification,and they can ensure good performance in classification accuracy at the same time.