摘要
针对旅游发展趋势及旅游景点评论观点抽取的难题,提出一种基于机器学习的多级规约条件随机场(conditional random field,CRF)模型的旅游景点评论观点分析方法。分析常见观点分析方法,利用CRF算法从数据集中提取评价观点,基于评论者关系特征构建景点虚假评论者自动识别模型对其评论观点进行过滤,并对评论观点的相关性进行观点合并。实验结果表明:该方法较其他常见方法在准确率、召回率方面有明显提高,且查准率-召回率曲线(precision and recall,PR)及接受者操作特征(receiver operating characteristic,ROC)曲线效果良好,对游客选择旅游目标具有很好的决策作用。
It’s difficult to extract the development trend of tourist and viewpoint for tourist attractions,propose the tourist attractions viewpoint analysis method based on machine learning multi-level constraints CRF model.Analyzes common review analysis method,use CRF algorithm to extract review from data set,and establish an automatic recognition model for false reviewers and their viewpoints based on the characteristics of the reviewer relationship,filter false reviewer viewpoints,and combine viewpoints with respect to the relevance of the review viewpoints.The experimental results show that,the method is better than other common methods in terms of precision and recall,and the PR and ROC curves are good,the method has a good decision-making function for the tourist to choose the traveling target.
作者
李川
马文胜
王瑞东
张少茹
Li Chuan;Ma Wensheng;Wang Ruidong;Zhang Shaoru(College of Computer,Xi’an Aeronautical University,Xi’an 710077,China;Health Science Center,Xi’an Jiaotong University,Xi’an 710061,China)
出处
《兵工自动化》
2020年第1期86-91,共6页
Ordnance Industry Automation
基金
国家自然科学基金(71373203)
西安航空学院2018年大学生创新创业训练计划项目(DCX2018056)
关键词
评论
规约
CRF
观点
review
constraint
CRF
viewpoints
