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I have a problem statement in which I have to classify the text data into various classes, but the training data is very less (250-300 data points for 4 classes). I am confused about what approach to use? Also is there any way that deep learning could be used for this problem with such a small tarining data?

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300 data points is enough to give good/average results with the traditional machine learning algorithms. You should test SVMs as they are considered good for text classification.

However, deep learning is very often used when you have a lot of data, which is not the case here. So I would not recommend it for your task.

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You can try bag of words approach to extract the features from text and apply any supervised ML classifier like Naive Bayes etc to classify.

Deep Learning requires a huge set of data so I don't think it can be applied here in this case.

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