So, I gathered political articles for my thesis, now I want to be able to classify given text. Though the classes distribution is actually crazy.

  • Class 1: 964 docs
  • Class 2: 37,020
  • Class 3: 640
  • Class 4: 2,675
  • Class 5: 793
  • Class 6: 23,160
  • Class 7: 2,665

Such a skewed data is obviously going to favor classes 2 and 6, though I thought about elevating the difference from last layer for classes with lower observations, is that worth a shot? Or it will actually create overfit for these classes? Unfortunately I can't scrap more data, the websites with articles doesn't have any more (at least now). Of course any data augmentation is not possible.

  • $\begingroup$ First thing you should try try is to classify the data as it is: if you're lucky, there are enough differences between the classes to avoid too much bias. Btw you probably meant "favor classes 2 and 6". $\endgroup$ – Erwan Dec 3 '20 at 22:41
  • $\begingroup$ Yeah, exactly - thanks for the answer, I was about to do it as you say $\endgroup$ – Mikołaj Wróblewski Dec 4 '20 at 12:59

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