Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing

• URL: http://arxiv.org/abs/2008.09976v1
• Date: Sun, 23 Aug 2020 06:34:05 GMT
• Title: Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing
• Authors: Cuiyun Gao, Jichuan Zeng, Zhiyuan Wen, David Lo, Xin Xia, Irwin King, Michael R. Lyu
• Abstract summary: We propose a novel emerging issue detection approach named MERIT. Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version. Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT.
• Score: 66.57888248681303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Millions of mobile apps are available in app stores, such as Apple's App Store and Google Play. For a mobile app, it would be increasingly challenging to stand out from the enormous competitors and become prevalent among users. Good user experience and well-designed functionalities are the keys to a successful app. To achieve this, popular apps usually schedule their updates frequently. If we can capture the critical app issues faced by users in a timely and accurate manner, developers can make timely updates, and good user experience can be ensured. There exist prior studies on analyzing reviews for detecting emerging app issues. These studies are usually based on topic modeling or clustering techniques. However, the short-length characteristics and sentiment of user reviews have not been considered. In this paper, we propose a novel emerging issue detection approach named MERIT to take into consideration the two aforementioned characteristics. Specifically, we propose an Adaptive Online Biterm Sentiment-Topic (AOBST) model for jointly modeling topics and corresponding sentiments that takes into consideration app versions. Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version, and automatically interpret the meaning of the topics with most relevant phrases and sentences. Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT in identifying emerging app issues, improving the state-of-the-art method by 22.3% in terms of F1-score. In terms of efficiency, MERIT can return results within acceptable time.

Related papers

• What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems [52.499838151272016]
  This paper addresses the "what-if" problem of estimating the values of key performance indicators (KPIs) that would have been obtained if a different app had been implemented by the radio access network (RAN) We propose a conformal-prediction-based counterfactual analysis method for wireless systems that provides reliable "error bars" for the estimated, containing the true with a user-defined probability.
  arXiv  Detail & Related papers  (2024-09-30T18:47:26Z)
• Fairness Concerns in App Reviews: A Study on AI-based Mobile Apps [9.948068408730654]
  This research aims to investigate fairness concerns raised in mobile app reviews. Our research focuses on AI-based mobile app reviews as the chance of unfair behaviors and outcomes in AI-based apps may be higher than in non-AI-based apps.
  arXiv  Detail & Related papers  (2024-01-16T03:43:33Z)
• User Driven Functionality Deletion for Mobile Apps [10.81190733388406]
  Evolving software with an increasing number of features is harder to understand and thus harder to use. Too much functionality can easily impact usability, maintainability, and resource consumption. Previous work showed that the deletion of functionality is common and sometimes driven by user reviews.
  arXiv  Detail & Related papers  (2023-05-30T19:56:54Z)
• Understanding Mobile App Reviews to Guide Misuse Audits [17.71313286969027]
  We leverage app reviews to identify exploitable apps and their functionalities that enable misuse. Stories by abusers and victims mostly focus on past misuses, whereas stories by third parties mostly identify stories indicating the potential for misuse. In total, we confirmed 156 exploitable apps facilitating the misuse.
  arXiv  Detail & Related papers  (2023-03-19T23:43:36Z)
• Proactive Prioritization of App Issues via Contrastive Learning [2.6763498831034043]
  We propose a new framework, PPrior, that enables proactive prioritization of app issues through identifying prominent reviews. PPrior employs a pre-trained T5 model and works in three phases. Phase one adapts the pre-trained T5 model to the user reviews data in a self-supervised fashion. Phase two, we leverage contrastive training to learn a generic and task-independent representation of user reviews.
  arXiv  Detail & Related papers  (2023-03-12T06:23:10Z)
• Integrating Rankings into Quantized Scores in Peer Review [61.27794774537103]
  In peer review, reviewers are usually asked to provide scores for the papers. To mitigate this issue, conferences have started to ask reviewers to additionally provide a ranking of the papers they have reviewed. There are no standard procedure for using this ranking information and Area Chairs may use it in different ways. We take a principled approach to integrate the ranking information into the scores.
  arXiv  Detail & Related papers  (2022-04-05T19:39:13Z)
• Analysis of Longitudinal Changes in Privacy Behavior of Android Applications [79.71330613821037]
  In this paper, we examine the trends in how Android apps have changed over time with respect to privacy. We examine the adoption of HTTPS, whether apps scan the device for other installed apps, the use of permissions for privacy-sensitive data, and the use of unique identifiers. We find that privacy-related behavior has improved with time as apps continue to receive updates, and that the third-party libraries used by apps are responsible for more issues with privacy.
  arXiv  Detail & Related papers  (2021-12-28T16:21:31Z)
• An Empirical Study on User Reviews Targeting Mobile Apps' Security & Privacy [1.8033500402815792]
  This study examines the privacy and security concerns of users using reviews in the Google Play Store. We analyzed 2.2M reviews from the top 539 apps of this Android market. It was evident from the results that the number of permissions that the apps request plays a dominant role in this matter.
  arXiv  Detail & Related papers  (2020-10-11T02:00:36Z)
• General-Purpose User Embeddings based on Mobile App Usage [46.343844014289246]
  behaviors on mobile app usage, including retention, installation, and uninstallation, can be a good indicator for both long-term and short-term interests of users. Traditionally, user modeling from mobile app usage heavily relies on handcrafted feature engineering. We present a tailored AutoEncoder-coupled Transformer Network (AETN), by which we overcome these challenges and achieve the goals of reducing manual efforts and boosting performance.
  arXiv  Detail & Related papers  (2020-05-27T12:01:50Z)
• Automating App Review Response Generation [67.58267006314415]
  We propose a novel approach RRGen that automatically generates review responses by learning knowledge relations between reviews and their responses. Experiments on 58 apps and 309,246 review-response pairs highlight that RRGen outperforms the baselines by at least 67.4% in terms of BLEU-4.
  arXiv  Detail & Related papers  (2020-02-10T05:23:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.