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Football Sentiment Analysis That Actually Understands Football

Generic sentiment tools see "he had a shocker" and don't know what to do with it. Ours knows it means the keeper let one in. 28 emotion categories trained on real football language, because fan sentiment is never just positive or negative.

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Why Generic Sentiment Analysis Falls Short

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Binary Is Useless

Positive/negative tells you nothing. Fans can be furious about tactics but proud of youth players in the same breath. You need topic-level emotion, not a thumbs up or down.

Football Language Is Unique

"What a banger." "Absolute shambles." "He's clear." Generic NLP built on product reviews and news articles can't parse the way fans talk.

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Context Gets Lost

"We were robbed" after a VAR decision is anger, not a crime report. Football sentiment needs football context that generic tools simply don't have.

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Volume Without Insight

Knowing 60% of tweets are "negative" after a loss tells you nothing you didn't already know. You need to know what they're negative about. The tactics? The referee? A specific player?

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"Can't I Just Ask ChatGPT?"

40% of LLM citations come from Reddit alone (Semrush, 2025). Ask an AI chatbot "what do fans think?" and you get a generic paragraph based on stale, single-platform data. No emotion tagging, no topic breakdown, no real-time match reaction, no fan filtering. They summarise. We analyse.

28 Emotions, Infinite Insight

Emotion Spectrum

The Full Spectrum of Fan Emotion

Frustration, hope, sarcasm, pride, despair, anger, amusement, disbelief, confidence and more. We capture how fans actually feel, not how a generic algorithm guesses they feel.

Topic-Tagged

Every Emotion Tagged to a Subject

Every emotion is tagged to its subject: manager, tactics, specific player, ownership, transfers, referee decisions. "Fans are 72% frustrated. About what?" We answer that.

Narratives

Narrative Detection Over Time

When sentiment around a topic shifts over weeks, we flag it. "Fan patience with the manager dropped 40% since October." That's a story you can lead with.

Evidence

Quotable Evidence

Selected fan posts that best represent each emotion cluster. Don't just say "fans are angry". Show exactly what they're saying and why, with representative quotes from real supporters.

28
distinct emotion categories
6+
social platforms analysed
4hrs
max report delivery (Creators tier)
£50
per month starting price

One report. Every platform. Every emotion.

Frequently Asked Questions

Fan Narrative detects 28 distinct emotion categories including frustration, hope, sarcasm, pride, despair, anger, amusement, confidence, resignation, disbelief, and more. This goes far beyond the binary positive/negative/neutral classification that generic sentiment tools offer, giving you granular insight into how fans actually feel about specific topics.

Start listening to the digital stands.
Right now, millions of fans are voluntarily talking online voicing their opinions.

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