Tinderbox's tagger mechanism also attempts to analyse the general tenor or sentiment of each note. Sentiment is measured on a scale from +1 to -1; an enthusiastic note like :
"This cheese is tasty, wholesome, and creamy. It is a delight!"
is scored near 1, whereas a critical note like:
"This cheese is rotten, slimy, and stale. It tastes terrible and should be thrown away."
is scored near -1.
The average sentiment for the entire note is stored in $Sentiment, and the score for each paragraph of the note is stored in $Sentiments.
Sentiment analysis is available in seven major languages using macOS 10.15 and later.
Why use this? What is it for?
One example would be a set of notes, gathered from magazines and newspapers, describing a performance or a social event in some past historical era. For this set of sources, tt would be interesting to know whether the writers were sympathetic or hostile to the event being commented upon.
For ancient sources, where there may at most four or five source, this can easily done 'manually (by reading/assessing the source texts). By comparison for a 19th century event, there might be dozens or hundreds of sources and automated assist could be helpful.
As ever with machine analysis of text it may miss, or misconstrue, nuances a trained human eye would catch. No matter, the automated assessment can still be a helpful 'serving' suggestion to inform more deliberate human assessment,