The hype: "Understand the sentiment in a block of text."
My problem is this: blocks of text do not contain a sentiment but a mixture of different sentiments, about different things. No way they would catch the difference between "and" and "but". Talk about "a dull knife"!
Actually: The idea of doing statistical analysis of blocks of text to derive a (non-existent) average sentiment is utter nonsense. It is guaranteed such "tools" will be useless. How long must we toil under an emperor with no clothes? I cannot express the level of distress I have about "researchers" who pass their text data through a black statistical box without understanding the math they are using and without bothering to read the text either. If they read the text, it would quickly become evident that opinions, if subtle, are mixed: consumers "like A" and "dislike B" all at the same time. So trying to turn this into an average opinion is pointless....except to the people who think they are doing research, using off-the-shelf garbage and maybe even getting PhD's. Weirdly enough, companies like Microsoft brag about tools that incorporate "decision tree algorithms". That is 2 strikes against them at the start: using crap math [decision trees aren't even good statistics], and believing in average opinions.
The poor innocent public thinks AI is a done deal and will be more and more in our lives. I strongly suspect the emperor will prevent other contenders for the throne - causing lasting damage, well beyond the short sighted, Bayesian, dreams of the moment.
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Don't forget, my key technical claim about AODiagrams and DataEquilibrium, is that they are a statistical model as good as the Bayesian one. They still suffer from the same issue.
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