基于互联网文本挖掘的用户意图感知

摘要

能否准确感知用户意图是创新设计的关键,传统用户意图研究方法费时费力且容易忽略重要因素.论文提出,用户意图表达的是用户对一个对象的期望集合,而基于该对象分布在互联网的海量数据隐藏着这个集合的所有因素.通过互联网文本挖掘技术获取该用户意图有关的因素,并把这些因素与用户意图等价起来.为了验证它们的等价关系,论文以可穿戴智能设备为研究对象,采用正反问卷法对因素集合的真实度进行测量.同时,论文从多个维度分析了文本挖掘获取因素的科学性,并与传统用户意图研究方法进行交叉对比,研究结果表明基于互联网文本挖掘技术的用户意图求解方案具有可行性且优点较多.论文所用方法适用于所有类似用户意图这种复杂对象的建模与求解.%The accuracy of user intent perception is the key to innovative design. The research methods of traditional user intent are time-consuming and prone to overlooking some of important factors. User intent is proposed to be a factor set of user expectations for an object, and all the factors are hidden in the object-relational massive data distributed on the internet which can be achieved by text mining technology. The equal value relation between user intent and factor set is proved by a pro and con questionnaire survey. Meanwhile, the factors acquired by text mining are testified to have advantage over traditional methods. The feasibility of the above-mentioned proposal is verified by taking wearable intelligent devices as a study, and the result is approving. The method proposed in this paper is applicable to all modeling and solving of complex objects which is similar to user intent.