论文标题
需求：确定包含客户需求的微博博客数据
Needmining: Identifying micro blog data containing customer needs
论文作者
论文摘要
新产品和服务的设计始于确定潜在客户或用户的需求。许多现有的方法，例如观察，调查和实验，借鉴了特定的努力，以引起个人不满意的需求。同时，微型博客中大量的用户生成的内容可以免费访问。尽管已经分析了此信息以监视对现有产品的观点，但尚未为启发需求而进行利用。在本文中，我们为这项工作奠定了重要的基础：我们提出了一种机器学习方法来识别那些表达需求的帖子。我们对E-Obility域中推文的评估表明，可以以显着的精确或召回结果来识别相关推文的较小份额。应用于庞大的数据集，开发的方法应为创新经理提供可扩展的需求启发支持 - 跨成千上万的用户，从而增加了他可用的服务设计工具。
The design of new products and services starts with the identification of needs of potential customers or users. Many existing methods like observations, surveys, and experiments draw upon specific efforts to elicit unsatisfied needs from individuals. At the same time, a huge amount of user-generated content in micro blogs is freely accessible at no cost. While this information is already analyzed to monitor sentiments towards existing offerings, it has not yet been tapped for the elicitation of needs. In this paper, we lay an important foundation for this endeavor: we propose a Machine Learning approach to identify those posts that do express needs. Our evaluation of tweets in the e-mobility domain demonstrates that the small share of relevant tweets can be identified with remarkable precision or recall results. Applied to huge data sets, the developed method should enable scalable need elicitation support for innovation managers - across thousands of users, and thus augment the service design tool set available to him.
