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Consumer product reviews are an invaluable source of data because theycontain a wide range of information that could help requirement engineers tomeet user needs. Recent studies have shown that tweets about softwareapplications and reviews on App Stores contain useful information, which enablea more responsive software requirements elicitation. However, all of thesestudies' subjects are merely software applications. Information on systemsoftware, such as embedded software, operating systems, and firmware, areoverlooked, unless reviews of a product using them are investigated. Challengesin investigating these reviews could come from the fact that there is a hugevolume of data available, as well as the fact that reviews of such products arediverse in nature, meaning that they may contain information mostly on hardwarecomponents or broadly on the product as a whole. Motivated by theseobservations, we conduct an exploratory study using a dataset of 7198 reviewsentences from 6 Internet of Things (IoT) products. Our qualitative analysisdemonstrates that a sufficient quantity of software related information existsin these reviews. In addition, we investigate the performance of two supervisedmachine learning techniques (Support Vector Machines and Convolutional NeuralNetworks) for classification of information contained in the reviews. Ourresults suggest that, with a certain setup, these two techniques can be used toclassify the information automatically with high precision and recall.

Do users talk about the software in my product? Analyzing user reviews on IoT products