A Pragma-Semantic Analysis of the Emotion/SentimentRelation in Debates
Bibliographic record
Abstract
In the last years, emotions recognition tools have become more and more popular, aiming at detecting the emotions of human actors while performing different intelligent tasks by means of headsets and facial emotions detection tools.In addition to this kind of technology, when participants interact with each others by means of textual exchanges, sentiment analysis techniques, from the natural language processing research area, are exploited to detect the polarity of the exchanged messages.Investigating how these two connected components interacts and can support each other towards a better emotions and sentiment detection is a relevant but unexplored research challenge.In this paper, we start from a dataset of debate interactions annotated with the emotions of the involved participants, captured by means of EEG headsets and a facial emotions recognition tool, and the argumentative structures of the debates, and we compare this information to the polarity of the proposed textual arguments, retrieved through a sentiment analysis algorithm.A pragma-semantic analysis of the obtained results is provided, along with a discussion of the potential future work.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".