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Do consumers talk about the software in my product? An Exploratory Study of IoT Products on Amazon

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Názov: Do consumers talk about the software in my product? An Exploratory Study of IoT Products on Amazon
Autori: Kamonphop Srisopha, Barry Boehm, Pooyan Behnamghader
Zdroj: CLEI Electronic Journal, Vol 22, Iss 1 (2019)
Informácie o vydavateľovi: Centro Latinoamericano de Estudios en Informática, 2019.
Rok vydania: 2019
Zbierka: LCC:Electronic computers. Computer science
Predmety: Passive Crowdsourcing, Internet of Things, User Reviews, Text Classification, Software Evolution, Empirical Study, Electronic computers. Computer science, QA75.5-76.95
Popis: 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.
Druh dokumentu: article
Popis súboru: electronic resource
Jazyk: English
ISSN: 0717-5000
Relation: http://www.clei.org/cleiej/index.php/cleiej/article/view/270; https://doaj.org/toc/0717-5000
DOI: 10.19153/cleiej.22.1.1
Prístupová URL adresa: https://doaj.org/article/2780a37d0ef24c4d99e34493d0e89c61
Prístupové číslo: edsdoj.2780a37d0ef24c4d99e34493d0e89c61
Databáza: Directory of Open Access Journals
Popis
Abstrakt: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.
ISSN:07175000
DOI:10.19153/cleiej.22.1.1