An NLP platform that reads social media at scale and turns it into sentiment and emotion insight brand teams can act on.
Brand teams needed to understand how a brand was actually perceived across social media — not just volume, but sentiment and emotion — at a scale no human team could read manually.
Social data is vast, messy, and nuanced. Extracting reliable sentiment and emotion from it, and presenting it as something a brand manager can act on, required serious NLP and thoughtful product design.
We built pipelines to ingest and process high volumes of social media data continuously.
NLP models classify not just positive/negative sentiment but emotional tone, giving a richer read on brand perception.
Dashboards surface trends, spikes, and themes so brand teams can see what's moving and respond.
Brand sentiment only becomes a useful signal at volume — a handful of posts is anecdote, thousands per day across topics and time is signal. The ingestion layer pulls from social platform APIs and data providers on a continuous basis, normalising very different post formats (text length, language, embedded media references) into a common schema before anything touches the NLP layer. Deduplication and spam/bot filtering happen early, because unfiltered social data is dominated by noise that would otherwise swamp genuine brand-relevant signal.
A single positive/negative sentiment score collapses a lot of nuance that brand teams actually care about — 'frustrated' and 'disappointed' are both negative sentiment but call for different responses. The NLP pipeline scores text on sentiment (positive/negative/neutral) and separately on emotion categories (joy, anger, frustration, trust, and others relevant to brand contexts), using fine-tuned language models rather than off-the-shelf lexicon-based scoring, which performs poorly on the sarcasm, slang, and brand-specific shorthand that dominates real social text.
Per-post scores aren't the deliverable — a brand team doesn't read individual posts at scale, they need trends: is sentiment around a campaign improving week over week, which topics are driving negative emotion this month, how does this compare to a competitor mention. The aggregation layer rolls per-post scores up by brand, topic, time window, and (where available) demographic or geographic dimensions, feeding dashboards that surface trends and outliers rather than raw counts. Alerting on significant sentiment shifts — a sudden spike in negative emotion around a specific topic — runs on top of the same rollups.
Brand teams gained a continuous, data-backed read on perception — able to spot shifts in sentiment and emotion early and respond with evidence rather than instinct.
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