What do we mean in Sentiment Analysis NLP when it is said a sentence is positive or negative? I think I need to specify this regarding any other parameter. For example "iPhone is good" is a positive one respect to Apple Company, however that is negative one in respect to Samsung.
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Human emotions are infinitely complex. However, humans emotions are sometimes reduced to the binary classification of positive or negative. That reduction makes the problem more tractable for machine learning.
In your example, the term "iPhone" can be labeled be as positive because the word "good" can be labeled as positive.
There should be caution generalizing a single sentence to entities that are not included (i.e., "Apple" or "Samsung").
answered Sep 7, 2021 at 18:33
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$\begingroup$ So, you mean that it doesn’t depend on the subject? For example if I say “ terrorists are good” and “ students are good”, they evaluated the same? I am actually taking about nn based like BERT not something like VADER. $\endgroup$ Commented Sep 7, 2021 at 19:55
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1$\begingroup$ A model like BERT trained on real world data will map and interpolate the sentiment from the labels with which it was trained. That means it will likely know that
terroristhas a negative connotation and thatgoodadds a positive sentiment to the noun it modifies. Asgood terroristis a rare combination (on its own, without more context), it is hard to know how a learned model like BERT would rate its sentiment, given the juxtaposition. $\endgroup$– n1k31t4Commented Sep 7, 2021 at 20:58