Prioritizing App Reviews for Developer Responses on Google Play

• URL: http://arxiv.org/abs/2502.01520v1
• Date: Mon, 03 Feb 2025 16:56:08 GMT
• Title: Prioritizing App Reviews for Developer Responses on Google Play
• Authors: Mohsen Jafari, Forough Majidi, Abbas Heydarnoori,
• Abstract summary: Since 2013, Google Play has allowed developers to respond to user reviews. Only 13% to 18% of developers engage in this practice. We propose a method to prioritize reviews based on response priority.
• Score: 1.5771347525430772
• License:
• Abstract: The number of applications in Google Play has increased dramatically in recent years. On Google Play, users can write detailed reviews and rate apps, with these ratings significantly influencing app success and download numbers. Reviews often include notable information like feature requests, which are valuable for software maintenance. Users can update their reviews and ratings anytime. Studies indicate that apps with ratings below three stars are typically avoided by potential users. Since 2013, Google Play has allowed developers to respond to user reviews, helping resolve issues and potentially boosting overall ratings and download rates. However, responding to reviews is time-consuming, and only 13% to 18% of developers engage in this practice. To address this challenge, we propose a method to prioritize reviews based on response priority. We collected and preprocessed review data, extracted both textual and semantic features, and assessed their impact on the importance of responses. We labelled reviews as requiring a response or not and trained four different machine learning models to prioritize them. We evaluated the models performance using metrics such as F1-Score, Accuracy, Precision, and Recall. Our findings indicate that the XGBoost model is the most effective for prioritizing reviews needing a response.

Related papers

• Improving Code Reviewer Recommendation: Accuracy, Latency, Workload, and Bystanders [6.538051328482194]
  We build upon the recommender that has been in production since 2018 RevRecV1. We find that reviewers were being assigned based on prior authorship of files. Having an individual who is responsible for the review, reduces the time take for reviews by -11%.
  arXiv  Detail & Related papers  (2023-12-28T17:55:13Z)
• UltraFeedback: Boosting Language Models with Scaled AI Feedback [99.4633351133207]
  We present textscUltraFeedback, a large-scale, high-quality, and diversified AI feedback dataset. Our work validates the effectiveness of scaled AI feedback data in constructing strong open-source chat language models.
  arXiv  Detail & Related papers  (2023-10-02T17:40:01Z)
• 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)
• SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation [48.1799451277808]
  We propose a Sentiment-aware Interactive Fusion Network (SIFN) for review-based item recommendation. We first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review. Then, we propose a sentiment prediction task that guides the sentiment learner to extract sentiment-aware features via explicit sentiment labels.
  arXiv  Detail & Related papers  (2021-08-18T08:04:38Z)
• Can We Automate Scientific Reviewing? [89.50052670307434]
  We discuss the possibility of using state-of-the-art natural language processing (NLP) models to generate first-pass peer reviews for scientific papers. We collect a dataset of papers in the machine learning domain, annotate them with different aspects of content covered in each review, and train targeted summarization models that take in papers to generate reviews. Comprehensive experimental results show that system-generated reviews tend to touch upon more aspects of the paper than human-written reviews.
  arXiv  Detail & Related papers  (2021-01-30T07:16:53Z)
• ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis [62.76038841302741]
  We build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison. Experimental results show that our review score predictor reaches 71.4%-100% accuracy. Human assessment by domain experts shows that 41.7%-70.5% of the comments generated by ReviewRobot are valid and constructive, and better than human-written ones for 20% of the time.
  arXiv  Detail & Related papers  (2020-10-13T02:17:58Z)
• Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing [66.57888248681303]
  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.
  arXiv  Detail & Related papers  (2020-08-23T06:34:05Z)
• App-Aware Response Synthesis for User Reviews [7.466973484411213]
  AAR Synth is an app-aware response synthesis system. It retrieves the top-K most relevant app reviews and the most relevant snippet from the app description. A fused machine learning model integrates the seq2seq model with a machine reading comprehension model.
  arXiv  Detail & Related papers  (2020-07-31T01:28:02Z)
• 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.