Traditional Sentiment Analysis Falls Short
Most sentiment models take one of two approaches:
They assess the emotional tone of the article, interpreting the author's language to determine whether it sounds positive or negative.
Or, they judge the subject matter itself — for example, labeling articles about conflict, disease, or crisis as automatically negative.
But both methods have serious flaws, especially when applied to news media. Journalists aim for impartiality, often using neutral language even when reporting on highly charged topics. Similarly, certain subjects (like war or climate change) may be inherently negative, even when a company’s involvement is actually positive — such as leading humanitarian aid or advancing clean energy.
Polecat’s New Approach: Entity-Centric Sentiment
Polecat’s AI sentiment model changes the game. Rather than simply reading tone or judging subject matter, our model uses the contextual intelligence of a Large Language Model (LLM) to evaluate how the event impacts your selected entity.
In other words, it asks:
Is this event good or bad news for the company, brand, or topic I care about?
This approach means:
Positive actions in negative contexts (e.g., a company leading disaster relief) are accurately recognized.
Neutral reporting doesn't dilute the real sentiment effect on your entity.
Your reputation insights are more precise, more relevant, and more actionable.
Why It Matters
In an era of complex global events and fast-moving narratives, understanding how your organization is perceived — not just what’s happening — is critical. Polecat’s entity-centric sentiment model delivers a sharper, more meaningful view of your reputation landscape, helping you respond smarter and faster.