So I have this situation, I have filtered a bunch of single independent sentences that I filtered because they contain the word X (in my case, X = "budget"). if the meaning of the sentence is "budget increases/goes up/etc" my result is 1 and, if not, 0. all sentences hold such meaning (the word budget is not used in different ways)

this is not a traditional "sentiment" problem and I do not know how to approach this. I could run into a variety of cases ("budget did not go down" // "A budget cut was in order" // "A budget cut was not in order")

if required, I can categorize by hand up to 400/500 sentences and say with certainty the result (0, 1). could you point me to any model that would solve this? would the amount of hand-labeled samples be enough?


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This seems like a straight forward classification problem: "Increased", "Decreased" (I assume "Neither Increased nor Decreased" can be a third category as well). Something simple like a Support Vector Machine (SVM) could work.

That being said, only 400-500 example cases aren't a lot so you might run into overfitting especially if you try to run whatever model you land on later down the road.


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