时态文本数据流特征流行趋势模型及算法

Temporal Text Data Stream Feature Trend Model and Algorithm

当今在电商和社交等平台上每天会产生大量的文本数据流。快速提取文本数据流的特征并将其用于发现一些事物的趋势变化来指导企业运营十分重要,比如服装企业必须尽可能快速而又准确地感知流行信息,服装特征的流行趋势对设计生产与经营起着至关重要的作用。以线上商品的文本数据流为研究对象,结合线上的销售文本实时数据流,定义了商品的时态文本数据流特征趋势模型,然后提出了一种文本数据流特征趋势发现的实时挖掘算法。将该算法应用到服装销售的文本描述以提取流行特征应用,可以获得有效的服装流行趋势,为企业制定生产计划、选择营销策略提供了决策支持。使用电商平台的真实销售数据进行实验,结果证明:该算法提取流行特征的准确率较高、速度较快,具有重要的理论与实际意义。

Today,on the platform of e-commerce and social networking,there will be a lot of text data streams.It is very important to extract the characteristics of text data flow quickly to find some trend for guiding the operation of enterprises.For example,clothing enterprises must perceive popular information as quickly and accurately as possible.Fashion trends are of vital importance to the design,production and operation.Taken the text data flow of online goods as the research object,combining the online sales text real-time data flow,this paper defined a characteristic trend model of the temporal text data flow.Then,it proposed a real-time mining algorithm for text data stream feature trend finding.The algorithm was applied on the description of clothing sales text to extract popular feature applications.It can obtain an effective fashion trend and provide decision support for enterprises to formulate production plans and select marketing strategies.On the real sales data of the e-commerce platform,the experiment results prove that the algorithm has good accuracy and fast speed.Therefore,the proposed algorithm has important theoretical and practical significance.