AOBTM: Adaptive Online Biterm Topic Modeling for Version Sensitive Short-texts Analysis
Bibliographic record
Abstract
Analysis of mobile app reviews has shown its important role in requirement engineering, software maintenance and evolution of mobile apps. Mobile app developers check their users' reviews frequently to clarify the issues experienced by users or capture the new issues that are introduced due to a recent app update. App reviews have a dynamic nature and their discussed topics change over time. The changes in the topics among collected reviews for different versions of an app can reveal important issues about the app update. A main technique in this analysis is using topic modeling algorithms. However, app reviews are short texts and it is challenging to unveil their latent topics over time. Conventional topic models such as Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) suffer from the sparsity of word co-occurrence patterns while inferring topics for short texts. Furthermore, these algorithms cannot capture topics over numerous consecutive time-slices (or versions). Online topic modeling algorithms such as Online LDA (OLDA) and Online Biterm Topic Model (OBTM) speed up the inference of topic models for the texts collected in the latest time-slice by saving a fraction of data from the previous time-slice. But these algorithms do not analyze the statistical-data of all the previous time-slices, which can confer contributions to the topic distribution of the current time-slice.In this paper, we propose Adaptive Online Biterm Topic Model (AOBTM) to model topics in short texts adaptively. AOBTM alleviates the sparsity problem in short-texts and considers the statistical-data for an optimal number of previous time-slices. We also propose parallel algorithms to automatically determine the optimal number of topics and the best number of previous versions that should be considered in topic inference phase. Automatic evaluation on collections of app reviews and real-world short text datasets confirm that AOBTM can find more coherent topics and outperforms the state-of-the-art baselines. For reproducibility of the results, we open source all scripts.
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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 | 0.014 |
| 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 | 0.003 |
| 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".