基于情绪词与情感词协作学习的情感分类方法研究  

Research on Sentiment Classification of Collaborative Learning Based on Emotion Words and Sentiment Words

作　　者：代大明[1,2] 李寿山[1,2] 李培峰[1,2] 朱巧明[1,2] 

机构地区：[1]江苏省计算机信息处理技术重点实验室,苏州215006 [2]苏州大学计算机科学与技术学院,苏州215006

出　　处：《计算机科学》2012年第12期241-244,共4页Computer Science

基　　金：国家自然科学基金(60970056;61070123;61003155);高等学校博士学科点专项科研基金(20093201110006);模式识别国家重点实验室开放课题基金资助

摘　　要：情感分类任务旨在自动识别文本所表达的情感色彩信息(例如,褒或者贬、支持或者反对)。提出一种基于情绪词与情感词协作学习的情感分类方法:在基于传统情感词资源的基础上,引入少量情绪词辅助学习,只利用大规模未标注数据实现情感分类。具体来讲,基于文档-单词二部图的标签传播算法框架,利用情绪词与情感词构建两个视图,通过协作学习的方法从大规模未标注语料中抽取高正确率的自动标注样本作为训练数据,然后训练分类器进行情感分类。实验表明,该方法在多个领域的情感分类任务中都取得了较好的分类效果。Sentiment classification aims to distinguish the expressed sentiment categories by the text,such as positive vs.negative and agree vs.disagree.We used a opinion lexicon,together with a small scale of emotion keyword to conduct sentiment classification with unlabeled data.Specifically,a document-word bipartite graph was builts,and then the opinion words and emotion words were served as labeled points while the documents were regarded as unlabeled points in the graph.Label propagation algorithm was used to propagate the label information of the words to the documents.Finally,the high confident automatically-labeled samples were used as training data for sentiment classification through collaborative learning method.Experimental results demonstrate that our approach achieves a good performance for sentiment classification across multiple domains.

关 键 词：情绪词 情感词 二部图 标签传播算法 协作学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]