Semantic role labeling of emotions in tweets
Bibliographic record
Abstract
Past work on emotion processing has focused solely on detecting emotions, and ignored questions such as `who is feeling the emotion (the experiencer)?' and `towards whom is the emotion directed (the stimulus)?'. We automatically compile a large dataset of tweets pertaining to the 2012 US presidential elections, and annotate it not only for emotion but also for the experiencer and the stimulus. The annotations reveal that most of tweets express emotions of the tweeter, and only a few are indicative of the emotions of someone else. We then develop a classi er for detect- ing emotion that obtains an F-score of 55.86 on a 7-way classi cation task. Finally, we show how the stimulus iden- ti cation task can also be framed as a classi cation task, circumventing more complicated problems of detecting en- tity mentions and coreferences. Our classi er outperforms competitive baselines and obtains an F-score of 58.30.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".