Ethnic minorities' mentality and homosexuality psychology in literature: A text emotion analysis with NRC lexicon
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
As a significant subfield of natural language processing (NLP), text emotion analysis has been extensively researched and applied in various domains, such as media, education, and medicine. It has shown significant results in annotating blog posts that rely on an extensive corpus of short phrases. However, in interdisciplinary fields like literary pragmatics, character emotion analysis in literature becomes crucial. Despite the importance of this topic, there are fewer studies, especially for niche subjects such as ethnic minorities' mentality and homosexuality psychology. This paper examines the effectiveness of the widely used lexicon National Research Council of Canada (NRC) in detecting metaphorical words in the famous homosexual novel Maurice. To increase the accuracy of the test, we classified and cleaned the stop words using the Natural Language Toolkit (NLTK) before the analysis step. Our results indicate that the lexicon is able to demonstrate reasonable emotional changes in the story.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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