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"I'm even doubtful AI is on the horizon" Agreed. I dont think this paper was the one I was looking for, but it will do. [1] The authors are working on handli
by ahartman00 8y ago
"I'm even doubtful AI is on the horizon"
Agreed. I dont think this paper was the one I was looking for, but it will do. [1] The authors are working on handling negation in sentiment analysis. Ie, this movie is good, this movie is not good, this movie is not very good.
"One of the most important sub tasks in sentiment analysis is to
determine the sequence of words affected by negation. Most of the existing sentiment analysis systems used
traditional methods based on static window and punctuation marks to determine the scope of negation.
However, these methods do not offer satisfactory performance due to variability in the negation scope,
inability to deal with linguistic features and improper word sense disambiguation. " [2]
[1] Sorry to be in a hurry, i just think this point is important given the hype around AI, and all the talk about using AI to "manage" human conversations. Look up AI winter if you want to learn about hype, AI, and expectations :)
[2] http://www.jcomputers.us/vol12/jcp1205-11.pdf http://www.jcomputers.us/vol12/jcp1205-11.pdf
- sonnyblarney 8y agoThose are good points. But the more important issue will be context. For example, in order for sentiment to be 'racist' - the ethnic nature of the participants may have to be known. So it's not about 'analyzing a statement' so much as it is understanding so much about people, individuals, current thresholds for tolerance, current events. Etc. etc.. It's impossible to look at a 'post' in total isolation, it has to be put in the context of the real world which is way beyond AI today. So it's not possible to 'train' AI simply by putting 'sentiment' as an input parameter. You'd have to 'train' NN's with inputs that include the entirety of CNN, Wikipedia and pop culture!