Scale to estimate the aspect-oriented sentiment polarity under anaphors influence (SPAI)
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Purpose The purpose of this paper is to handle the anaphors through anaphora resolution in aspect-oriented sentiment analysis. Sentiment analysis is one of the predictive analytics of social media. In particular, the social media platform Twitter is an open platform to post the opinion by subscribers on contextual issues, events, products, individuals and organizations. Design/methodology/approach The sentiment polarity assessment is not deterministic to conclude the opinion of the target audience unless the polarity is assessed under diversified aspects. Hence, the aspect-oriented sentiment polarity assessment is a crucial objective of the opinion assessment over social media. However, the aspect-oriented sentiment polarity assessment often influences by the curse of anaphora resolution. Findings Focusing on these limitations, a scale to estimate the aspects oriented sentiment polarity under anaphors influence has been portrayed in this article. To assess the aspect-based sentiment polarity of the tweets, the anaphors of the tweets have been considered to assess the weightage of the tweets toward the sentiment polarity. Originality/value The experimental study presents the performance of the proposed model by comparing it with the contemporary models, which are estimating the sentiment polarity tweets under anaphors impact.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".