Discourse structure and attitudinal valence of opinion words in sentiment extraction
Bibliographic record
Abstract
Taboada et al. (2008) propose a word-based method for extracting sentiment from text that relies on the most relevant parts of a text. The method predicts that opinion words found in the nuclei (more important parts) of a document are more significant for the overall sentiment, whereas opinion words found in the satellites (less important parts) only potentially interfere with the overall sentiment. However, as pointed out by Taboada et al. (2008) and Narayanan et al. (2009), for certain discourse relations (for instance, Condition relations), the calculation of sentiment should involve both parts of the relation. Based on our analysis of the affective content expressed by automatically extracted discourse relations from the Simon Fraser University Corpus (Taboada 2008) and the Penn Discourse Treebank (Prasad et al. 2008), we propose to classify all the discourse relations into four categories: (1) relations that reverse polarity, (2) intensify polarity, (3) downtone polarity, or (4) produce no change in polarity. We compare the performance of a sentiment analysis system (SO-CAL, Taboada et al. 2011) when opinion words are detected only in the nuclei with its performance when both parts of the relation are analyzed in combination with the opinion words. The results of the experiment show that extraction of both the nucleus and the satellite parts of texts does not improve the performance of a sentiment extraction system.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".