A Semantic Metadata Enrichment Software Ecosystem based on Sentiment and Emotion Metadata Enrichments
Bibliographic record
Abstract
Information retrieval and analysis is frequently used to extract meaningful knowledge from the unstructured web and long texts. As existing computer search engines struggle to understand the meaning of natural language, semantically sentiment and emotion enriched metadata may improve search engine capabilities and user finding. A semantic metadata enrichment software ecosystem (SMESE) has been proposed in our previous research. This paper presents an enhanced version of this ecosystem with a sentiment and emotion metadata enrichments algorithm. This paper proposes a model and an algorithm enhancing search engines finding contents according to the user interests, through text analysis approaches for sentiment and emotion analysis. It presents the design, implementation and evaluation of an engine harvesting and enriching metadata related to sentiment and emotion analysis. It includes the SSEA (Semantic Sentiment and Emotion Analysis) semantic model and algorithm that discover and enrich sentiment and emotion metadata hidden within the text or linked to multimedia structure. The performance of sentiment and emotion analysis enrichments is evaluated using a number of prototype simulations by comparing them to existing enriched metadata techniques. The results show that the algorithm SSEA enable greater understanding and finding of document or contents associated with sentiment and emotion enriched metadata.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".