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Sentiment Mix
Sentiment & Article Volume
Daily average sentiment with article count
Author Sentiment
Sentiment by author & outlet · Top 5
Recent Articles
Latest coverage for this topic
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30 day data across all topics and outlets
Topic Comparison
Avg sentiment by topic (30d)
Topic + Outlet
Sentiment by outlet (30d snapshot)
No outlet data available.
About the Media Intelligence Platform
StrawberryData ingests headlines, full articles, transcripts, and contextual metadata from thousands of global news and media sources. Each item is processed using consistent sentiment analysis, topic detection, narrative framing analysis, author attribution, and outlet-level normalization so metrics remain comparable across time, outlets, regions, and subject areas.
Our Pro solutions include:
- Sentiment – Normalized scoring from –1 to 1 capturing tone, emphasis, emotional context, and narrative direction across headlines and article text.
- Headline framing & narrative bias – Detection of framing patterns, emphasis shifts, and language cues that reveal how stories are positioned across outlets.
- Coverage volume & intensity – Rolling headline and article counts showing cadence, amplification cycles, and spikes in media attention.
- Source & outlet mix – Breakdowns by publication, region, and ecosystem to reveal where narratives originate and how different media environments frame the same topic.
- Author sentiment – Per-author tone analysis highlighting consistent framing tendencies, bias patterns, and narrative posture across beats and publications.
- Recent coverage & drivers – Identification of the latest stories driving sentiment shifts, volatility, and emerging risk signals.
- Topic comparisons – Benchmark sentiment, volume, and framing across peers, competitors, industries, public figures, or related issues.
These insights are designed for teams in communications, public affairs, investor relations, reputation and risk intelligence, competitive strategy, research, media analysis, and news analytics.
All metrics originate from one or a combination of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) pipelines and may occasionally reflect sampling noise when outlets change formats or publishing behavior; such cases are flagged and continuously refined across models and data sources.