Attitude in Reported and Non-reported News: A Critique of Sentiment Analysis in Corpus Pragmatics
Bibliographic record
Abstract
Abstract This study uses natural language processing (NLP) tools to examine a large Canadian English-language news corpus with respect to quotation and positive/negative sentiment. Specifically, we analyse sentiment in reported/quoted speech in comparison to non-quoted speech, testing the hypothesis that quoted speech contains negative sentiment and is more subjective. Our study explores whether NLP tools that simplify pragmatically complex concepts (such as attitude/evaluation/stance) can be used to test hypotheses that derive from discourse analytic or pragmatic studies of news discourse. We show that sentiment analysis is not suitable for accurate analysis of the news values of Positivity and Negativity and cannot be used to test hypotheses that derive from assumptions about these news values. At the same time, some of the insights from the sentiment analysis confirm our hypotheses (and are in line with other corpus studies), and sentiment results can be a starting point for additional qualitative analysis. Finally, we suggest a range of possible developments for sentiment analysis which draw on linguistic considerations.
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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 | 0.160 | 0.350 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".