How Chattermill Defines Overall Sentiment

Sentiment in Chattermill is generated by a machine learning model trained on labelled feedback text.

This model looks only at the written comments, not the numeric scores (NPS, CSAT, star ratings, etc.), and assigns sentiment to each theme mentioned in the text — for example, Delivery, Customer Service, Pricing, etc.

That means a single comment can include a mix of positive, neutral, and negative sentiments depending on what’s being said about each theme.
There isn’t one “unified sentiment” per feedback item.

Numeric ratings such as scores or NPS values are treated separately from text sentiment.
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They’re often used for reporting or comparison, and are typically grouped into these tiers:

This mapping is standard across most datasets unless a client has custom rules.
It helps make ratings and text sentiment easier to compare in dashboards, even though they are calculated independently.

Each theme gets its own sentiment; the system doesn’t try to create a single “overall” sentiment label.

Instead, it’s possible to calculate a Net Sentiment value — the average of all theme-level sentiments, where:

So, a piece of feedback with both positive and negative mentions might end up with a Net Sentiment close to 0 (neutral overall).