AOBTM: Adaptive Online Biterm Topic Modeling for Version Sensitive\n Short-texts Analysis
Bibliographic record
Abstract
Analysis of mobile app reviews has shown its important role in requirement\nengineering, software maintenance and evolution of mobile apps. Mobile app\ndevelopers check their users' reviews frequently to clarify the issues\nexperienced by users or capture the new issues that are introduced due to a\nrecent app update. App reviews have a dynamic nature and their discussed topics\nchange over time. The changes in the topics among collected reviews for\ndifferent versions of an app can reveal important issues about the app update.\nA main technique in this analysis is using topic modeling algorithms. However,\napp reviews are short texts and it is challenging to unveil their latent topics\nover time. Conventional topic models suffer from the sparsity of word\nco-occurrence patterns while inferring topics for short texts. Furthermore,\nthese algorithms cannot capture topics over numerous consecutive time-slices.\nOnline topic modeling algorithms speed up the inference of topic models for the\ntexts collected in the latest time-slice by saving a fraction of data from the\nprevious time-slice. But these algorithms do not analyze the statistical-data\nof all the previous time-slices, which can confer contributions to the topic\ndistribution of the current time-slice.\n We propose Adaptive Online Biterm Topic Model (AOBTM) to model topics in\nshort texts adaptively. AOBTM alleviates the sparsity problem in short-texts\nand considers the statistical-data for an optimal number of previous\ntime-slices. We also propose parallel algorithms to automatically determine the\noptimal number of topics and the best number of previous versions that should\nbe considered in topic inference phase. Automatic evaluation on collections of\napp reviews and real-world short text datasets confirm that AOBTM can find more\ncoherent topics and outperforms the state-of-the-art baselines.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".