sentiments

 It's pretty amazing how the development of AI research is now intertwining with psychology. Not really surprising, since if the current ML models emulate the human thinking, this is what should be happening, but the amazing part is that the development of the AI research has finally reached this point.

The IEEE Computing Edge magazine tends to be rather boring but usually contains one gem per issue. In the November 2021 issue that gem was the history of Ikonas graphics processing (I didn't realize that the raster video started to appear only in mid-1970s, before then the memory costs were prohibitively expensive, and the displays did vector graphics in hardware). In the December 2021 issue that gem is an article about the human emotions "The hourglass model revisited" (https://www.sentic.net/hourglass-model-revisited.pdf, 10.1109/MIS.2020.2992799).

They define the human emotions as a combination of 6 dimensions, 4 of them that can be positive or negative (hence forming a "hourglass", thin in the middle, growing towards extremes): sensitivity, attitude, temper, introspection, and 2 neutral that can be present or not present: expectation and surprise. Additionally they split the attitude into "towards self" and towards others".

The gradations of 4 "main" emotions from positive to negative are defined there as:

Introspection: ecstasy-joy-contentment-melancholy-sadness-grief

Temper: bliss-calmness-serenity-annoyance-anger-rage

Attitude: delight-pleasantness-acceptance-dislike-disgust-loathing

Sensitivity: enthusiasm-eagerness-responsiveess-anxiety-fear-terror

And then they define the compounds, for example "hate =  anger + fear", or "pessimism = expectation + sadness". Though strangely they define despair in the same way as pessimism and not as "expectation + grief", I guess this model has quite a bit of subjectivity.

They also define the absolute strength of an emotion (in range of -1 to +1) as a sum of strengths of its components (or we could say "of its present components") divided by the count of its present (i.e. non-0) components. 

This is an improvement on always dividing by 4, but I wonder if it could be improved by just taking a sum of highest negative and highest positive components. Their formula already shifts the weights in the same direction compared to the previous one, and arguably presence of a little bit of a side emotion would not reduce the effect of the strongest one, and maybe even add to it if both emotions are in the same direction. So maybe it should be for each direction not even

  max(abs(E_i))

but

  1-Product(1-abs(E_i))

Then I've remembered where I've seen the same problem: in the Bayesian deduction! Just rescale every sentiment from [-1, 1] to [0, 1] and consider it to be a Bayesian probability of an evidence that the sentiment is positive. And then just compose them in the Bayesian deduction, in a classic formula or with formula by odds/chances as described in https://babkin-cep.blogspot.com/2017/02/a-better-explanation-of-bayes-adaboost.html. Then the sentiments that are at 0 would naturally translate to 0.5 and won't affect the computation.