Mobile and location-based social media applications provide platforms for users to share brief opinions about products, venues, and services. These quickly typed opinions, or microreviews, are a valuable source of current sentiment on a wide variety of subjects. However, there is currently little research on how to mine this information to present it back to users in easily consumable way. In this paper, we introduce the task of microsummarization, which combines sentiment analysis, summarization, and entity recognition in order to surface key content to users. We explore unsupervised and supervised methods for this task, and find we can reliably extract relevant entities and the sentiment targeted towards them using crowdsourced labels as supervision. In an end-to-end evaluation, we find our best-performing system is vastly preferred by judges over a traditional extractive summarization approach. This work motivates an entirely new approach to summarization, incorporating both sentiment analysis and item extraction for modernized, at-a-glance presentation of public opinion.
Improving the energy efficiency of smartphones is critical for increasing the utility that they provide to the users. With most mobile operating systems, users are responsible for managing their phone's battery efficiency by utilizing the various settings provided by the operating system, as well as selecting energy-efficient apps. However, current app marketplaces do not provide users with information about app energy efficiency, which makes it challenging for the user to make informed decision when selecting an app. This paper presents a novel machine learning approach to estimate app energy efficiency by utilizing textual information available in the Google Play store such as an app's description, user reviews, as well as system permissions. Our detailed analysis of the resulting system shows that hardware permissions, app description, and user reviews correlate well with energy efficiency ratings. We evaluate five models that represent popular classes of machine learning algorithms in their ability to predict energy efficiency ratings. Finally, we compare our approach to gold truth ratings obtained by the actual energy profiling of the app, demonstrating that the proposed system is able to estimate an app's energy efficiency within less than 1 point on the 1-5 scale provided by the profiler, without requiring any kind of profiling. CCS CONCEPTS • Human-centered computing → Ubiquitous and mobile devices; • Hardware → Power estimation and optimization;
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