> For the complete documentation index, see [llms.txt](https://docs.pipfeed.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pipfeed.com/how-do-we-calculate-sentiment.md).

# How do we calculate sentiment?

AFINN is a list of words rated for valence with an integer between minus five (negative) and plus five (positive). Sentiment analysis is performed by cross-checking the string tokens (words, emojis) with the AFINN list and getting their respective scores. The comparative score is simply: the sum of each token/number of tokens.

This approach leaves you with a mid-point of 0 and the upper and lower bounds are constrained to positive and negative 5 respectively (the same as each token! 😸). For example, let’s imagine an incredibly “positive” string with 200 tokens where each token has an AFINN score of 5. Our resulting comparative score would look like this:

(max positive score \* number of tokens) / number of tokens\
(5 \* 200) / 200 = 5


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