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Automating App Review Response Generation Based on Contextual Knowledge
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A systematic literature review: Opinion mining studies from mobile app store user reviews
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"What Parts of Your Apps are Loved by Users?" (T)
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Mining User Opinions in Mobile App Reviews: A Keyword-Based Approach (T)
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Automating App Review Response Generation
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Which Version Should Be Released to App Store?
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PAID: Prioritizing app issues for developers by tracking user reviews over versions
inferredcites →https://doi.org/10.1109/issre.2016.33openalex-snapshot
Combining Word Embedding with Information Retrieval to Recommend Similar Bug Reports
inferredcites →https://doi.org/10.1109/ms.2015.149openalex-snapshot
Is It Worth Responding to Reviews? Studying the Top Free Apps in Google Play
inferredcites →https://doi.org/10.1109/re.2014.6912257openalex-snapshot
How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews
inferredcites →https://doi.org/10.1109/tse.2012.71openalex-snapshot
Trustrace: Mining Software Repositories to Improve the Accuracy of Requirement Traceability Links
inferredcites →https://doi.org/10.1109/tse.2018.2864217openalex-snapshot
Chaff from the Wheat: Characterizing and Determining Valid Bug Reports
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Probabilistic Principal Component Analysis
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Facilitating developer-user interactions with mobile app review digests
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AR-miner: mining informative reviews for developers from mobile app marketplace
inferredcites →https://doi.org/10.1145/2884781.2884818openalex-snapshot
Release planning of mobile apps based on user reviews
inferredcites →https://doi.org/10.1145/2897073.2897076openalex-snapshot
Mobile app and app store analysis, testing and optimisation
inferredcites →https://doi.org/10.1145/2950290.2950299openalex-snapshot
What would users change in my app? summarizing app reviews for recommending software changes
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Clustering Mobile Apps Based on Mined Textual Features
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A Feature-Oriented Sentiment Rating for Mobile App Reviews
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Online app review analysis for identifying emerging issues
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Deep Reinforcement Learning for Dialogue Generation
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Nematus: a Toolkit for Neural Machine Translation
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
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Neural Responding Machine for Short-Text Conversation
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Sentiment strength detection in short informal text
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What are you complaining about?: a study of online reviews of mobile applications
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A systematic literature review
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Multi-grained Aspect Fusion for Review Response Generation
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How to effectively mine app reviews concerning software ecosystem? A survey of review characteristics
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Large-Scale Analysis of User Feedback on AI-Powered Mobile Apps
inferred← citeshttps://doi.org/10.1109/icodsa67155.2025.11157547openalex-snapshot
Generative AI Models for Automated User Review Feedback on Digital Platforms
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Toward Enhancing Subtitle Features in Mobile Apps: Analyzing User Reviews for Accessibility and Usability Insights
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Supporting Online Customer Feedback Management with Automatic Review Response Generation
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Automatic App Review Using Android Application
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Fine-tuning Open-source Large Language Models for Automated Response to Customer Feedback
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