摘要： 网络评论短文本的细粒度情感分析是文本挖掘的研究热点，评价对象作为细粒度情感分析的基础，在识别文本过程中具有重要作用，如何充分利用上下文信息并对其进行有效表示是评价对象识别的难点所在。提出一种结合词特征与语义特征的评价对象识别方法。针对商品评论语料，使用条件随机场进行评价对象识别，在词特征、依存句法特征的基础上引入语义特征，并将各特征进行组合，以充分利用上下文信息，提高评价对象的识别准确性。在手机评论和酒店评论2个数据集上进行实验，结果表明，该方法的识别准确性较高，且F值分别高达75.36%和82.64%。

关键词: 评价对象识别, 细粒度情感分析, 语义特征, 特征组合, 条件随机场

Abstract: The fine-grained sentiment analysis of short texts in online comment is a research hotspot of text mining.As the basis of fine-grained sentiment analysis,evaluation objects play an important role in the process of identifying texts.How to make full use of context information and effectively represent is the object of evaluation difficulty.Therefore,combining the word feature and semantic feature,an evaluation object identification method is proposed.According to the commodity review corpus,the Conditional Random Fields(CRFs) are used to identify the object of comment,and the semantic feature is introduced on the basis of word feature and dependent syntax feature.Different features are combined to make full use of context information and improve the recognition accuracy of the comment object.Experiments on two data sets of mobile phone reviews and hotel reviews show that the accuracy of the method is higher,and the F values are 75.36% and 82.64%respectively.

Key words: comment object recognition, fine-grained sentiment analysis, semantic feature, feature combination, Conditional Random Fields(CRFs)

中图分类号: 

• TP391.1