Bibliographic record
Abstract
Subjectivity analysis recognizes the contextual polarity of opinions, attitudes, emotions, feelings etc. regarding products, services, topics, or issues. Subjectivity classification categorizes the given text as subjective or objective. While an objective text contains one or more facts about a product or an issue, a subjective text expresses author's opinions. Statistical analysis shows that subjectivity analysis of social issues is different from that of products. This paper focuses on subjectivity analysis of social issues. Subjectivity of a document strongly depends on its sentences. Hence, a lexical-syntactical approach is proposed to recognize and classify subjectivity at the sentence level. This approach considers the role of various opinion terms especially verbs on opinions regarding social issues. Evaluation of the proposed approach on a data-set consisting comments about abortion shows that it slightly outperforms other similar works. It has a good accuracy especially on the strong sentences which express explicit opinions. Its reasonable F-measure demonstrates a good balance between the precision and recall which makes it suitable for applications such as sentiment polarity classification, text sentiment summarization, and opinion question answering.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".