I was reading articles on sentiment analysis and NLP and there is something I cant quite understand. One of the methods to label a dataset is to use something like textblob with a polarity dictionary that would count words in a positive and negative dictionary and give a score based on it.
Then the dataset is used to train a classification algorithm. My question is, why do we bother with ML at all while we have a rule-based labeling method that we trust so much to the point we consider it as the ground truth?

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  • $\begingroup$ Are you saying not to bother with ML, since you could run this thru a program, do a lookup of words, and compute a total plus or minus score without the help of ML? I think you are referring to a very simplistic sentiment method, and yes you could probably do it yourself. $\endgroup$ 2 days ago


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