Identifying leading indicators of product recalls from online reviews using positive unlabeled learning and domain adaptation

Consumer protection agencies are charged with safeguarding the public from hazardous products, but the thousands of products under their jurisdiction make it challenging to identify and respond to consumer complaints quickly. From the consumer's perspective, online reviews can provide evidence of product defects, but manually sifting through hundreds of reviews is not always feasible. In this paper, we propose a system to mine Amazon.com reviews to identify products that may pose safety or health hazards. Since labeled data for this task are scarce, our approach combines positive unlabeled learning with domain adaptation to train a classifier from consumer complaints submitted to the U.S. Consumer Product Safety Commission. On a validation set of manually annotated Amazon product reviews, we find that our approach results in an absolute F1 score improvement of 8% over the best competing baseline. Furthermore, we apply the classifier to Amazon reviews of known recalled products; the classifier identifies reviews reporting safety hazards prior to the recall date for 45% of the products. This suggests that the system may be able to provide an early warning system to alert consumers to hazardous products before an official recall is announced

Deep Learning-Based User Feedback Classification in Mobile App Reviews

As online users are interacting with many mobile apps under different usage contexts, user needs in an app design process have become a critical issue. Existing studies indicate timely and constructive online reviews from users become extremely crucial for developers to understand user needs and create innovation opportunities. However, discovering and quantifying potential user needs from large amounts of unstructured text is a nontrivial task. In this paper, we propose a domain-oriented deep learning approach that can discover the most critical user needs such as app product new features and bug reports from a large volume of online product reviews. We conduct comprehensive evaluations including quantitative evaluations like F-measure a, and qualitative evaluations such as a case study to ensure the quality of discovered information, specifically, including the number of bug reports and feature requests. Experimental results demonstrate that our proposed supervised model outperforms the baseline models and could find more valuable information such as more important keywords and more coherent topics. Our research has significant managerial implications for app developers, app customers, and app platform providers

A Comparative Study on TF-IDF feature Weighting Method and its Analysis using Unstructured Dataset

Text Classification is the process of categorizing text into the relevant categories and its algorithms are at the core of many Natural Language Processing (NLP). Term Frequency-Inverse Document Frequency (TF-IDF) and NLP are the most highly used information retrieval methods in text classification. We have investigated and analyzed the feature weighting method for text classification on unstructured data. The proposed model considered two features N-Grams and TF-IDF on the IMDB movie reviews and Amazon Alexa reviews dataset for sentiment analysis. Then we have used the state-of-the-art classifier to validate the method i.e., Support Vector Machine (SVM), Logistic Regression, Multinomial Naive Bayes (Multinomial NB), Random Forest, Decision Tree, and k-nearest neighbors (KNN). From those two feature extractions, a significant increase in feature extraction with TF-IDF features rather than based on N-Gram. TF-IDF got the maximum accuracy (93.81%), precision (94.20%), recall (93.81%), and F1-score (91.99%) value in Random Forest classifier.Comment: 10 pages, 3 figures, COLINS-2021, 5th International Conference on Computational Linguistics and Intelligent Systems, April 22-23, 2021, Kharkiv, Ukrain

Privacy concerns and benefits of engagement with social media-enabled apps: A privacy calculus perspective

Privacy threats in a social media-enabled application (app) can originate from either the institution or other app users. Although privacy in social media is well studied, the role of social (peer) privacy concerns is largely unknown and most privacy studies on mobile apps focus on initial adoption and ignore long-term behavioral outcomes. Drawing on the privacy calculus theory, this study examines the impact of both institutional and social privacy concerns on long-term user engagement with social media-enabled apps. Findings from the analysis of 354 survey responses reveal that both institutional and social privacy concerns decrease engagement. Regarding the antecedents, the perceived sensitivity of information increases institutional privacy concerns. However, social privacy concerns are influenced by the perception of risk and control. Moreover, while the impacts of social and enjoyment benefits are expectedly positive, the perception of efficiency benefits decreases engagement. These findings are further investigated and validated through a follow-up text analysis study, suggesting that users who enjoy the functionality of these apps are more likely to express social privacy concerns and minimize their engagement. This study contributes to the literature of privacy on mobile apps by unraveling the intricate dynamics of privacy concerns and benefits in the social mobile era

Microgrids

Microgrids are a growing segment of the energy industry, representing a paradigm shift from centralized structures toward more localized, autonomous, dynamic, and bi-directional energy networks, especially in cities and communities. The ability to isolate from the larger grid makes microgrids resilient, while their capability of forming scalable energy clusters permits the delivery of services that make the grid more sustainable and competitive. Through an optimal design and management process, microgrids could also provide efficient, low-cost, clean energy and help to improve the operation and stability of regional energy systems. This book covers these promising and dynamic areas of research and development and gathers contributions on different aspects of microgrids in an aim to impart higher degrees of sustainability and resilience to energy systems

Proceedings of the 6th International Conference EEDAL'11 Energy Efficiency in Domestic Appliances and Lighting

This book contains the papers presented at the sixth international conference on Energy Efficiency in Domestic Appliances and Lighting. EEDAL'11 was organised in Copenhagen, Denmark in May 2011. This major international conference, which was previously been staged in Florence 1997, Naples 2000, Turin 2003, London 2006, Berlin 200h9a s been very successful in attracting an international community of stakeholders dealing with residential appliances, equipment, metering liagnhdti ng (including manufacturers, retailers, consumers, governments, international organisations aangde ncies, academia and experts) to discuss the progress achieved in technologies, behavioural aspects and poliacineds , the strategies that need to be implemented to further progress this important work. Potential readers who may benefit from this book include researchers, engineers, policymakers, and all those who can influence the design, selection, application, and operation of electrical appliances and lighting.JRC.F.7-Renewable Energ

Networked world: Risks and opportunities in the Internet of Things

The Internet of Things (IoT) – devices that are connected to the Internet and collect and use data to operate – is about to transform society. Everything from smart fridges and lightbulbs to remote sensors and cities will collect data that can be analysed and used to provide a wealth of bespoke products and services. The impacts will be huge - by 2020, some 25 billion devices will be connected to the Internet with some studies estimating this number will rise to 125 billion in 2030. These will include many things that have never been connected to the Internet before. Like all new technologies, IoT offers substantial new opportunities which must be considered in parallel with the new risks that come with it. To make sense of this new world, Lloyd’s worked with University College London’s (UCL) Department of Science, Technology, Engineering and Public Policy (STEaPP) and the PETRAS IoT Research Hub to publish this report. ‘Networked world’ analyses IoT’s opportunities, risks and regulatory landscape. It aims to help insurers understand potential exposures across marine, smart homes, water infrastructure and agriculture while highlighting the implications for insurance operations and product development. The report also helps risk managers assess how this technology could impact their businesses and consider how they can mitigate associated risks