Needmining：识别包含客户需求的微博数据
Needmining: Identifying micro blog data containing customer needs
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，如观察、调查和实验，都是通过具体的努力来引出个人未满足的需求。与此同时，微博上大量的用户原创内容可以免费获取。虽然已经对这些信息进行了分析，以监测对现有产品的看法，但尚未利用这些信息来激发需求。在本文中，我们为这一努力奠定了重要的基础：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。我们的评估，在电子移动领域的推文表明，相关的推文的小份额可以识别显着的精度或召回结果。应用到庞大的数据集，开发的方法应该使可扩展的需求启发支持创新经理-在成千上万的用户，从而增加了服务设计工具集提供给他。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
