Reader Perspective Emotion Analysis in Text through Ensemble based Multi-Label Classification Framework
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
Multiple emotions are often triggered in readers in response to text stimuli like news article. In this paper, we present a novel method for classifying news sentences into multiple emotion categories using an ensemble based multi-label classification technique called RAKEL. The emotion data consists of 1305 news sentences and the emotion classes considered are disgust, fear, happiness and sadness. Words are the most obvious choice as feature for emotion recognition. In addition to that we have introduced two novel feature sets: polarity of subject, verb and object of the sentences and semantic frames. Experiments concerning the comparison of features revealed that semantic frame feature combined with polarity based feature performs best in emotion classification. Experiments on feature selection over word and semantic frame features have been performed in order to handle feature sparseness problem. In both word and semantic frame feature, improvements in the overall performance have been observed after optimal feature selection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.000 | 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