Mining Collective Opinions for Comparison of Mobile Apps
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
User review is a crucial component of open mobile app market such as Google Play Store. These markets allow users to submit feedback for downloaded apps in the form of a) start ratings and b) opinions in the form of text reviews. Users read these reviews in order to gain insight into the app before they buy or download it. The user opinion about the product also influence on the purchasing decisions of potential users; indeed play a key role in the generation of revenue for the developers. The mobile apps can contain large volumes of reviews and it is impossible for a user to skim through thousands of reviews to find the opinion of other users about the features he/she is interested in. Towards this end, we propose a methodology to automatically extract the features of an app from its corresponding reviews using machine learning technique. Moreover, our proposed methodology aid user to compare the features across multiple apps, using the sentiments, expressed in their associated reviews. The proposed methodology can be used to understand user's preference to a certain mobile app and can uncover the relational behind why users prefer an app over other.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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