Review-Pulse: A Dashboard for Managing User Feedback for Android Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to the large volume of data and its unstructured nature, managing user feedback via application (app) reviews is a significant challenge for Android developers. This study presents a dashboard to streamline this process using advanced machine learning and analysis techniques. The dashboard employs a fine-tuned Generative Pretrained Transformer (GPT-3.5) model to detect and categorize issues in user reviews automatically. Additional dashboard features include sentiment and toxicity analysis to provide insights into user emotions, potentially negative feedback, and code analysis to identify code smells across different app versions. We conducted a pilot study to evaluate the usability and effectiveness of the dashboard. The results indicate that the dashboard is user-friendly and effective in helping developers manage user feedback and monitor code quality. However, certain limitations were identified, such as dependency on the quality of training data and potential inaccuracies in sentiment and toxicity analysis. This dashboard aims to aid developers in effectively managing app reviews, prioritizing issues, and maintaining high app quality to improve user satisfaction. Tool URL: https://tdresearchgroup.github.io/Review-Pulseldashboard/ Demo Video: https://youtu.be/cT6su8dqh2g
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it