# Release early, release often, and watch your users' emotions

arXiv: https://arxiv.org/abs/1906.06403 · HTML: https://mlsanity.com/paper/1906.06403/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [GUing: A Mobile GUI Search Engine using a Vision-Language Model](https://mlsanity.com/paper/2405.00145/) (2024)
- [TM-TREK at SemEval-2024 Task 8: Towards LLM-Based Automatic Boundary Detection for Human-Machine Mixed Text](https://mlsanity.com/paper/2404.00899/) (2024)
- [Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approach](https://mlsanity.com/paper/2408.17404/) (2024)
- [Using Voice and Biofeedback to Predict User Engagement during Product Feedback Interviews](https://mlsanity.com/paper/2104.02410/) (2021)
- [FeedAIde: Guiding App Users to Submit Rich Feedback Reports by Asking Context-Aware Follow-Up Questions](https://mlsanity.com/paper/2603.04244/) (2026)
- [LikeThis! Empowering App Users to Submit UI Improvement Suggestions Instead of Complaints](https://mlsanity.com/paper/2603.04245/) (2026)

## Abstract (arXiv)

App stores are highly competitive markets, sometimes offering dozens of apps for a single use case. Unexpected app changes such as a feature removal might incite even loyal users to explore alternative apps. Sentiment analysis tools can help monitor users' emotions expressed, e.g., in app reviews or tweets. We found that these emotions include four recurring patterns corresponding to the app releases. Based on these patterns and online reports about popular apps, we derived five release lessons to assist app vendors maintain positive emotions and gain competitive advantages.