GDELT global-news company tone (daily)
Daily enrichment signals scoring how the world's news talks about ~70 US and Canadian companies. Each day the 96 keyless GDELT 2.1 GKG fifteen-minute bulk files for the last complete UTC day are downloaded; articles are deduplicated on URL, their V2ENHANCEDORGANIZATIONS are resolved against a curated company dictionary (name, primary-listing ticker, HQ country, aliases — shared with the hn_company_sentiment series so curation cannot drift), and GDELT's own per-article V1.5TONE scores (computed from full article text) are aggregated per company and day. Per company: n_articles, n_sources, mean/median/std/min/max tone, pos_share / neg_share (fraction of articles with tone >= 1.0 / <= -1.0), mean_pos_words_pct / mean_neg_words_pct, dominant_theme, tone_signal (positive / negative / polarized / none), and news_tone_score = 100 * (55% min-max-normalized mean_tone + 25% min-max-normalized pos_share + 20% min-max-normalized (1 - neg_share)), ranked as tone_rank (1 = most positive; ties: n_articles desc, company name asc). Who joins this: a quant desk joins per-ticker daily global-news tone to their positions/returns table on ticker + as_of; an IR/comms team joins on company_name to their media-monitoring calendar. Primary key: (as_of, company_name); join keys: ticker, company_name, as_of, country_code (ISO alpha-3). Nullability: ticker is null for private companies (OpenAI, Anthropic, Stripe, ...); std_tone is null for single-article companies; dominant_theme is null when no themes were recorded. Caveats: news_tone_score is a within-snapshot relative measure, not comparable across days; GDELT tone is computed on machine-translated text for non-English articles; organization NER misses and corporate-suffix collisions are possible despite suffix stripping. No article text and no per-row URLs are stored. Sample use: filter ticker = 'TSLA' for Tesla's daily global-news tone, or order by tone_rank for today's most positively covered companies.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 26
- Colonnes
- 21
- Cadence de la source
- Quotidienne
- Dernière actualisation
- 27 sept. 2026
- Thème
- news
| Colonne | Type | Description |
|---|---|---|
| as_of | string | Snapshot date (UTC, YYYY-MM-DD) — the complete UTC day whose 96 GKG slots were collected. Part of the primary key. (unit: date) |
| company_name | string | Canonical company name from the connector's curated dictionary. Part of the primary key; one of the join keys for media-monitoring tables. (unit: text) |
| ticker | string | Primary-listing stock symbol (exchange-suffixed for TSX, e.g. SHOP.TO). Null for private companies (OpenAI, Anthropic, Stripe, ...). The join key for positions/returns tables. (unit: symbol) |
| country_code | string | Company HQ country, ISO alpha-3 (USA/CAN). (unit: ISO alpha-3) |
| sector | string | Closed sector vocabulary from the connector (semiconductors, software, ai-lab, ...). (unit: category) |
| n_articles | integer | Number of distinct GDELT articles (URL- deduplicated within the day) mentioning the company. (unit: count) |
| n_sources | integer | Number of distinct publisher domains behind the company's articles. (unit: count) |
| mean_tone | float | Mean of GDELT's per-article V1.5TONE tone values (roughly [-10, 10]; positive is favorable). (unit: score) |
| median_tone | float | Median of the company's article tone values. (unit: score) |
| std_tone | float | Sample standard deviation of article tones; null when the company has a single article. (unit: score) |
| tone_min | float | Minimum article tone for the company that day. (unit: score) |
| tone_max | float | Maximum article tone for the company that day. (unit: score) |
| pos_share | float | Fraction of the company's articles with tone >= 1.0 (documented positive threshold). (unit: share) |
| neg_share | float | Fraction of the company's articles with tone <= -1.0 (documented negative threshold). (unit: share) |
| mean_pos_words_pct | float | Mean of GDELT's positive-word percentage across the company's articles. (unit: percent) |
| mean_neg_words_pct | float | Mean of GDELT's negative-word percentage across the company's articles. (unit: percent) |
| dominant_theme | string | Most frequent GDELT V1THEMES theme across the company's articles (count desc, alphabetical tie-break); null when no themes were recorded. (unit: text) |
| tone_signal | string | Categorical flag: 'positive' (mean_tone >= 3, >= 5 articles), 'negative' (mean_tone <= -3, >= 5 articles), 'polarized' (std_tone >= 3, >= 5 articles), else 'none'. (unit: category) |
| news_tone_score | float | 0-100 tone composite = 100 * (0.55 * min-max(mean_tone) + 0.25 * min-max(pos_share) + 0.20 * min-max(1 - neg_share)), min-maxed within the snapshot. Within-snapshot relative — not comparable across days. (unit: score) |
| tone_rank | integer | Rank by news_tone_score desc (1 = most positive); ties broken by n_articles desc, then company name asc. (unit: rank) |
| row_hash | string | Deterministic 16-hex-char content hash over (as_of, company_name, n_articles, mean_tone, dominant_theme) — identical input yields an identical snapshot. (unit: hash) |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| as_of | company_name | ticker | country_code | sector | n_articles | n_sources | mean_tone | median_tone | std_tone | tone_min | tone_max | pos_share | neg_share | mean_pos_words_pct | mean_neg_words_pct | dominant_theme | tone_signal | news_tone_score | tone_rank | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2026-09-26 | Airbnb | ABNB | USA | mobility | 1 | 1 | 6,891 | 6,891 | — | 6,891 | 6,891 | 1 | 0 | 6,891 | 0 | ECON_STOCKMARKET | none | 100 | 1 | 76103f03d5dd249d |
| 2026-09-26 | Intuit | INTU | USA | software | 1 | 1 | 2,464 | 2,464 | — | 2,464 | 2,464 | 1 | 0 | 3,913 | 1,449 | ECON_STOCKMARKET | none | 77,13 | 2 | c28192748fe856ee |
| 2026-09-26 | CoreWeave | CRWV | USA | cloud-infra | 1 | 1 | 2,227 | 2,227 | — | 2,227 | 2,227 | 1 | 0 | 3,981 | 1,754 | TAX_FNCACT | none | 75,9 | 3 | e82b08d6aa33cc91 |
| 2026-09-26 | Twilio | TWLO | USA | dev-tools | 1 | 1 | 1,566 | 1,566 | — | 1,566 | 1,566 | 1 | 0 | 2,428 | 0,861 | ECON_TAXATION | none | 72,49 | 4 | b5d2ab5f9a7aa8c9 |
| 2026-09-26 | CrowdStrike | CRWD | USA | cybersecurity | 3 | 2 | 1,451 | 1,339 | 0,194 | 1,339 | 1,675 | 1 | 0 | 4,333 | 2,882 | CRISISLEX_C07_SAFETY | none | 71,89 | 5 | f2bf1dac657295c7 |
| 2026-09-26 | Palo Alto Networks | PANW | USA | cybersecurity | 3 | 3 | 1,025 | 1,853 | 1,658 | -0,884 | 2,105 | 0,667 | 0 | 3,275 | 2,251 | CRISISLEX_C07_SAFETY | none | 61,37 | 6 | 3729a37b785a4b75 |
| 2026-09-26 | Broadcom | AVGO | USA | semiconductors | 24 | 8 | 1,098 | 1,134 | 0,763 | 0 | 2,524 | 0,542 | 0 | 2,697 | 1,598 | TAX_FNCACT | none | 58,62 | 7 | b8adb8a500c1b029 |
| 2026-09-26 | Tesla | TSLA | USA | hardware | 2 | 2 | 0,439 | 0,439 | 1,522 | -0,637 | 1,515 | 0,5 | 0 | 1,546 | 1,107 | TAX_FNCACT | none | 54,16 | 8 | 063c2a0e72af7a7c |
| 2026-09-26 | Intel | INTC | USA | semiconductors | 56 | 36 | 0,666 | 0,873 | 1,943 | -5,948 | 4,571 | 0,446 | 0,107 | 2,667 | 2,001 | TAX_FNCACT | none | 51,85 | 9 | 04f7218c26836661 |
| 2026-09-26 | Disney | DIS | USA | media | 194 | 106 | 0,636 | 0,636 | 2,253 | -7,706 | 7,527 | 0,448 | 0,191 | 3,519 | 2,883 | TAX_FNCACT | none | 50,06 | 10 | 03fc3af77563adfd |
- Actuelle
20260927T084041Z-a45d255f7e3f · sha256 a45d255f7e3f…
26 lignes · premier instantané
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/gdelt_company_tone/gdelt_company_tone_daily" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/gdelt_company_tone/gdelt_company_tone_daily").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)Point d’accès API : https://datazimuts.com/v1/datasets/gdelt_company_tone/gdelt_company_tone_daily
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.
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Épinglé à l’instantané 20260927T084041Z-a45d255f7e3f et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
GDELT global-news company tone (agent-curated). (2026). GDELT global-news company tone (daily) [Data set, snapshot 20260927T084041Z-a45d255f7e3f, sha256 a45d255f7e3f]. Datazimuts. Retrieved 2026-09-27, from https://datazimuts.com/fr/datasets/gdelt_company_tone/gdelt_company_tone_daily?snapshot=20260927T084041Z-a45d255f7e3f
@misc{dz_gdelt_company_tone_gdelt_company_tone_da_a45d255f,
title = {{GDELT global-news company tone (daily)}},
author = {{GDELT global-news company tone (agent-curated)}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/gdelt_company_tone/gdelt_company_tone_daily?snapshot=20260927T084041Z-a45d255f7e3f}},
note = {Snapshot 20260927T084041Z-a45d255f7e3f, sha256 a45d255f7e3f0dc454622cdcf504b561339c718a080328473c7d2c2bc0942b91; accessed 2026-09-27}
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<iframe src="https://datazimuts.com/embed/chart?dataset=gdelt_company_tone%2Fgdelt_company_tone_daily&lang=fr&theme=auto&snapshot=20260927T084041Z-a45d255f7e3f&x=as_of&y=n_articles&agg=avg" title="GDELT global-news company tone (daily)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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