用户是否会谈论我的产品中的软件？分析用户对IoT产品的评论
Do users talk about the software in my product? Analyzing user reviews on IoT products
摘要：
消费者产品评论是一个宝贵的数据来源，因为它们包含了广泛的信息，可以帮助需求工程师满足用户的需求。最近的研究表明，关于软件应用程序的推文和App Store上的评论包含有用的信息，这些信息可以更好地响应软件需求。然而，所有这些研究的主题仅仅是软件应用程序。有关系统软件（如嵌入式软件、操作系统和固件）的信息被忽略，除非对使用这些软件的产品进行审查。调查这些评论的挑战可能来自以下事实：存在大量可用数据，以及此类产品的评论性质多样，这意味着它们可能包含主要关于硬件组件或广泛关于整个产品的信息。受这些观察的启发，我们使用来自6个物联网（IoT）产品的7198个评论句子的数据集进行了探索性研究。我们的定性分析表明，足够数量的软件相关的信息存在于这些审查。此外，我们研究了两种监督机器学习技术（支持向量机和卷积神经网络）的性能分类的信息中包含的评论。我们的研究结果表明，在一定的设置，这两种技术可以用来自动分类的信息具有较高的精度和召回率。
英文摘要：
Consumer product reviews are an invaluable source of data because they contain a wide range of information that could help requirement engineers to meet user needs. Recent studies have shown that tweets about software applications and reviews on App Stores contain useful information, which enable a more responsive software requirements elicitation. However, all of these studies' subjects are merely software applications. Information on system software, such as embedded software, operating systems, and firmware, are overlooked, unless reviews of a product using them are investigated. Challenges in investigating these reviews could come from the fact that there is a huge volume of data available, as well as the fact that reviews of such products are diverse in nature, meaning that they may contain information mostly on hardware components or broadly on the product as a whole. Motivated by these observations, we conduct an exploratory study using a dataset of 7198 review sentences from 6 Internet of Things (IoT) products. Our qualitative analysis demonstrates that a sufficient quantity of software related information exists in these reviews. In addition, we investigate the performance of two supervised machine learning techniques (Support Vector Machines and Convolutional Neural Networks) for classification of information contained in the reviews. Our results suggest that, with a certain setup, these two techniques can be used to classify the information automatically with high precision and recall.
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。
