摘要
人们在网上消费前日益重视相关的评论,快速获取评论的有价值信息备受重视。文章基于网络评论中的普遍性特征,利用句法信息设计了主题算法,给出了主题分布的详细推理,在计算主题关键词时,设计了词相关性计算方法,采用惩罚因子调节横跨多个主题的关键词,使得关键词更精确反映本主题的内容。和标准主题模型对比,实验结果显示所设计的句法模型算法能清晰表示主题脉络,有助于人们获取海量评论中的关键信息。
People pay more attention to the relevant review from the network,how to obtain the core value with rapid automation has gotten the attention highly. Considering the sentences feature of online review,topic algorithm based on sentence is designed,a detailed inference process is provided. When computing the topic＇s key word,word relevance measure is proposed which penalizes the word frequency by a factor that captures how much the word is shared across topics,words for topics can been selected more accurately. Compared with the standard LDA,experiments show that the proposed model is better,there is a clearer topic cue.
作者
张世博
魏战红
ZHANG Shibo;WEI Zhanhong(Computer Department,Beijing Institute of Petrochemical Technology,Beijing 102600)
出处
《计算机与数字工程》
2018年第8期1593-1597,共5页
Computer & Digital Engineering
基金
国家自然科学基金项目(编号:61702040)
北京市教委科技计划项目(编号:KM201810017005)资助
关键词
网评信息
主题算法
惩罚调节
online reviews
topic algorithm
penalty regulation
