摘要
近年来,移动应用分布式平台不断扩大,用户评论越来越多,需求工程师需花费大量时间和精力从中提取改进或新增需求。针对这一问题,提出基于评论挖掘的需求获取方法 RERM,与已有方法不同的是,通过采用本体和条件随机场模型融合的特征提取方法,结合情感分析技术,可以对潜在软件需求进行分类型汇总,从细粒度上进行优先级排序和横向对比。实验结果表明,特征提取和情感分类算法性能良好,与其他方法比较,RERM提供了更多的有价值信息,提升了需求获取效率。
Recently,the distributed platform of mobile application has been constant expanding,user reviews are getting increased as well. Eliciting new / changed requirements from user reviews manually is a time and effort-consuming work for requirement engineers. In order to address this issue,this paper proposes an approach called RERM（ software requirement elicitation method based on review mining）. Different from existing work,by extracting software features via combining ontology with conditional random field（ CRF） model and analysing sentiment polarity,RERM can organise different requirement types for potential software and provide fine-grained prioritised sequence and lateral comparison. Experimental results show that feature extraction and sentiment classification perform well. Comparing with other methods,RERM provides more valuable information and improves the efficiency of requirement elicitation.
出处
《计算机应用与软件》
CSCD
2015年第8期28-33,共6页
Computer Applications and Software
基金
国家自然科学基金项目(91218302
61073044
61003028
71101138
61100071)
国家高技术研究发展计划项目(2012AA011206)
北京市自然科学基金项目(4122087)
国家重大科技专项(2012ZX01039-004)
关键词
评论挖掘
需求获取
本体模型
条件随机场
情感分类
Review mining Requirement elicitation Ontology model Conditional random field（CRF） Sentiment classification
