A Large Wordnet-based Sentiment Lexicon for Polish.
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
The applications of plWordNet, a very large wordnet for Polish, do not yet include work on sentiment and emotions. We present a pilot project to annotate plWordNet manually with sentiment polarity values and basic emotion values. We work with lexical units, plWordNet’s basic building blocks.1 So far, we have annotated about 30,000 nominal and adjectival LUs. The resulting lexicon is already one of the largest sentiment and emotion resources, in particular among those based on wordnets. We opted for manual annotation to ensure high accuracy, and to provide a reliable starting point for future semi-automated expansion. The paper lists the principal assumptions, outlines the annotation process, and introduces the resulting resource, plWordNetemo. We discuss the selection of the material for the pilot study, show the distribution of annotations across the wordnet, and consider the statistics, including interannotator agreement and the resolution of disagreement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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