MétaCan
ENFR
Menu
Cohort builder

4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

Export CSVExport JSONAPI query

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

Search term
Author
Year range
→
Sort
Language
Type
Field
Venue
Topic
Sentiment Analysis and Opinion Mining
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

797 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
797 works in the cohort · of 4,299,418page 6 of 16

Labels cover 0 of 797 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 797 of 797 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Identifying purpose behind electoral tweets
Saif M. Mohammad, Svetlana Kiritchenko, Joel Martin
2013· article· en· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
15
citations
affno abstractunlabeled
Sentiment Analysis of Arabic and English Tweets
Mohamed K. Elhadad, Kin Fun Li, Fayez Gebali
2019· book-chapter· en· Advances in intelligent systems and computing· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
14
citations
affno abstractunlabeled
Sentiment Tagging of Adjectives at the Meaning Level
Alina Andreevskaia, Sabine Bergler
2006· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
12
citations
affunlabeled
Sentiment Analysis for Streams of Web Data
Bruna Neuenschwander, Adriano C. M. Pereira, Wagner Meira, Denilson Barbosa
2014· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Using Google n-Grams to Expand Word-Emotion Association Lexicon
Jessica Perrie, Aminul Islam, Evangelos Milios, Vlado Kešelj
2013· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communicationconsensus · none
10
citations
← previousnext →

How this was built: Screen · Findings · About