基于复杂句式短文本情感分类研究  

Research on sentiment classification for complex sentence patterns of short text

作　　者：李毅捷 段利国[1,2] 李爱萍[1,2] LI Yijie;DUAN Liguo;LI Aiping(School of Computer Science and Technology,Taiyuan University of Technology,Jinzhong 030600,China;State Key Laboratory of Software Engineering,Wuhan University,Wuhan 430072,China)

机构地区：[1]太原理工大学计算机科学与技术学院,山西晋中030600 [2]武汉大学软件工程国家重点实验室,湖北武汉430072

出　　处：《现代电子技术》2018年第22期182-186,共5页Modern Electronics Technique

基　　金：武汉大学软件工程国家重点实验室开放课题(SKLSE2012-09-30);山西省自然科学基金资助项目(2013011015-2)~~

摘　　要：目前,网络文本中主观内容的情感倾向性识别成为文本信息处理的研究热点。针对汉语中复杂句式的结构特点以及对多种复杂句式的有效分析,基于word2vec进行情感词典的扩建,将扩充后的情感词典、关联词表、否定词表进行特征提取,得到有效的特征词序列,构建新的复杂句式模型并结合SVM进行训练和预测,完成复杂句式情感分类。实验结果表明,提出的复杂句式情感分类模型在处理精度方面比传统的句子级情感分类方法有了明显的提高,获得良好的情感分析效果。The sentiment tendency recognition of the subjective content in the current network text is a hot research topic of text information processing.In allusion to the structure characteristics of complex sentence patterns in Chinese and effective analysis of various complex sentence patterns,the sentiment dictionary is expanded based on the word2vec.Feature extraction is conducted for the expanded sentiment dictionary,associated word list,and negative word list,so as to obtain the effective se-quence of feature words.The new model of complex sentence patterns is established,which is trained and predicted by combin-ing with the SVM,so as to complete sentiment classification of complex sentence patterns.The experimental results show that,in comparison with the traditional sentence-level sentiment classification method,the proposed sentiment classification model of complex sentence patterns has a significant improvement in processing accuracy and can obtain a good sentiment analysis effect.

关 键 词：文本信息处理 情感分析 复杂句式 word2vec 情感分类模型 SVM 

分 类 号：TN911-34[电子电信—通信与信息系统] TP391.1[电子电信—信息与通信工程]