What is it about?
We examined thousands of app‑store reviews for two rental‑property platforms—Knimbus and MyLOFT—using the Appbot tool. By applying sentiment analysis and topic‑modeling, we identified what users like, dislike, and talk about most often. The study shows clear differences in satisfaction drivers, helping developers improve features, support, and overall user experience.
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Why is it important?
Practical relevance: Property‑tech companies can act on the identified pain points (e.g., payment glitches, navigation issues) to boost retention. Methodological contribution: Demonstrates a low‑cost, reproducible workflow that combines Appbot’s review extraction with open‑source sentiment and topic‑modeling libraries. Timeliness: As the rental‑market shifts to mobile‑first solutions, understanding real‑time user sentiment is critical for competitive advantage.
Perspectives
From our experience, the biggest surprise was how dramatically the same feature (e.g., “maintenance request”) was praised on MyLOFT but criticized on Knimbus, driven largely by differing UI flows. This underscores that even minor design tweaks can shift overall sentiment. We hope our transparent, step‑by‑step approach encourages other researchers and product teams to adopt similar analytics for their own apps.
Prof. Rupak Chakravarty
Panjab University
Read the Original
This page is a summary of: Comparative sentiment and topic analysis of user reviews for Knimbus and MyLOFT using Appbot, Performance Measurement and Metrics, September 2025, Emerald,
DOI: 10.1108/pmm-01-2022-0005.
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