Embedded Emotion-based Classification of Stack Overflow Questions Towards the Question Quality Prediction
Bibliographic record
Abstract
Software developers often ask questions in Stack Overflow Q & A site, and their posted questions sometimes do not meet the standard guidelines.As a consequence, some of the questions are edited by expert users, some of them are down-voted, or some are even deleted permanently.Besides, the users (i.e., developers) might not get the expected solutions for their problems.In this paper, we study up-voted and down-voted questions from Stack Overflow, and analyze the relationship of embedded emotions with question quality.We use Sentiment140 API for identifying embedded emotions in the question texts, and then apply Feed-Forward Multilayer Perceptron (MLP) and Support Vector Machine (SVM) on the emotion data for developing a quality prediction model.Experiments using 38,920 Stack Overflow questions suggest about 70% precision and about 74% recall for our model with 10-fold cross-validation, and these findings clearly reveal the impact of human emotions upon the quality of a question.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".