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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.

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.

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Sentiment Analysis and Opinion Mining
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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 ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
797 works in the cohort · of 4,299,418page 3 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.

venueno affunlabeled
Sentiment Analysis from Movie Reviews Using LSTMs
Jyostna Devi Bodapati, N. Veeranjaneyulu, Nagur Shareef Shaik
2019· article· fr· Ingénierie des systèmes d information· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+insufficient_payloadconsensus · insufficient_payload
57
citations
affunlabeled
Learning Emotion-enriched Word Representations
Ameeta Agrawal, Aijun An, Manos Papagelis
2018· article· en· International Conference on Computational Linguistics· Computer Science
distilled prediction:candidate · noneconsensus · none
51
citations
affno abstractunlabeled
Natural Language Processing for Social Media
Atefeh Farzindar, Diana Inkpen
2015· book· en· Synthesis lectures on human language technologies· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
48
citations
affunlabeled
CLaC and CLaC-NB
Alina Andreevskaia, Sabine Bergler
2007· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
47
citations
fundno affunlabeled
AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Abinew Ali Ayele, Nedjma Ousidhoum, David Ifeoluwa Adelani, Seid Muhie Yimam +19 more
2023· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
47
citations
affunlabeled
Contrastive analyses of evaluation in text
Maite Taboada, Marta Carretero
2012· article· en· Linguistics and the Human Sciences· Computer Science
distilled prediction:candidate · noneconsensus · none
47
citations
affunlabeled
Let Me Count the Words
Madeleine Pullman, Kelly A. McGuire, C. L. Cleveland
2005· article· en· Cornell Hotel and Restaurant Administration Quarterly· Computer Science
distilled prediction:candidate · noneconsensus · none
47
citations
affunlabeled
A Large Wordnet-based Sentiment Lexicon for Polish.
Monika Zaśko-Zielińska, Maciej Piasecki, Stan Śzpakowicz
2015· article· en· Recent Advances in Natural Language Processing· Computer Science
distilled prediction:candidate · noneconsensus · none
43
citations
affunlabeled
Can I Hear You? Sentiment Analysis on Medical Forums
Tanveer Ali, David Schramm, Marina Sokolova, Diana Inkpen
2013· article· en· International Joint Conference on Natural Language Processing· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
42
citations

How this was built: Screen · Findings · About