Attitude in Reported and Non-reported News: A Critique of Sentiment Analysis in Corpus Pragmatics
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
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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