摘要
为了解决文本情感分析的代价不平衡及静态决策中分类代价偏高的问题,文中考虑动态决策过程中产生的误分类代价和学习代价,构建基于序贯三支决策的代价敏感文本情感分析方法.首先,为了构建多粒度动态决策环境,提出针对文本数据的粒化模型.然后,引入序贯三支决策模型,构建动态文本分析框架.最后,利用真实文本评论数据集验证文中方法的有效性.实验表明文中方法在提高分类质量的同时,明显降低整体的决策代价.
To solve the problems of cost imbalance in text sentiment analysis and high classification cost in static decision-making,a cost-sensitive text sentiment analysis method is constructed based on sequential three-way decision,and the misclassification cost and learning cost in dynamic decision-making process are taken into account.Firstly,a granulation model for text data is proposed to construct a multi-level granular structure.Next,sequential three-way decision is introduced to set a dynamic text analysis framework.Finally,real text review datasets are utilized to validate the effectiveness of the proposed method.Experimental results show that the proposed method significantly reduces the overall decision-making cost with the improved classification quality.
作者
范琴
刘盾
叶晓庆
FAN Qin;LIU Dun;YE Xiaoqing(School of Economics and Management,Southwest Jiaotong University,Chengdu 610031)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2020年第8期732-742,共11页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61876157,71571148)
西南交通大学杨华学者A类计划(No.201806)资助。
关键词
情感分析
文本挖掘
三支决策
代价敏感
粒计算
Sentiment Analysis
Text Mining
Three-Way Decision
Cost-Sensitive
Granular Computing
