Smoothed Generalized Dirichlet: A Novel Count-Data Model for Detecting Emotional States
Bibliographic record
Abstract
In this article, we propose novel approaches to deal with the problem of burstiness, the challenge of count-data sparseness, and the curse of dimensionality. We introduce a smoothed generalized Dirichlet distribution that is a smoothed variant of the generalized Dirichlet distribution and a generalization of the smoothed Dirichlet. We provide different learning methods based on mixture models and agglomerative clustering-based geometrical information: Kullback–Leibler divergence, Fisher metric, and Bhattacharyya distance. Moreover, we show that the new smoothed generalized Dirichlet could be considered as a prior to the multinomial, which generates a new distribution for count data that we call the smoothed generalized Dirichlet multinomial. In particular, we present an approximation based on Taylor series expansion for better performance and optimized running time in the case of high-dimensional count data. The proposed models are evaluated through two emotion detection applications: disaster-tweet-related emotions and pain intensity estimation. Experiments show the efficiency and the robustness of our approaches when dealing with texts, videos, and images.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".