基于特征加权词向量的在线医疗评论情感分析

针对在线医疗评论文本具有行业专业性强、差异性大、不够规范等特点，提出一种基于特征加权词向量的在线医疗评论情感分析方法.利用Word2vec方法构建词向量模型，抽取情感词集合完善医疗服务领域情感词典，根据句法关系识别主题词与情感词的依存关系，引入期望交叉熵因子，建立特征加权词向量模型，分析在线医疗评论的情感倾向.实验结果表明扩充的医疗服务情感词典在分析性能上的准确率、召回率以及F₁值均高于基础情感词典，引入期望交叉熵因子后，基于特征加权词向量的情感分析方法在SVM分类上表现出更好的效果，体现了其在在线医疗评论挖掘领域的良好效用. 

Sentiment Analysis of Online Healthcare Reviews Based on Feature Weighted Word Vector

A sentiment analysis method of online healthcare reviews based on feature weighted word vector was proposed in view of the professional, diverse and less normative features of online healthcare reviews. The Word2vec method was used to construct the word vector model, and the sentiment word set was extracted to improve the sentiment lexicon in the field of healthcare service. The dependency between subject words and sentiment words was identified according to the syntactic relations. The expected cross entropy factor was introduced to establish a feature weighted word vector model to analyze the sentiment tendency of online healthcare reviews. The experimental results show that the accuracy, recall rate and F₁ value of the expanded healthcare service sentiment lexicon are higher than those of the basic sentiment lexicon. After the introduction of the expected cross entropy factor, the sentiment analysis method based on the feature weighted word vector shows better effect in the SVM classification, which reflects its good utility in the online healthcare reviews mining.