I have to classify very large amounts of text in over 10,000 categories. I will need expert advice because I am still a student.
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My data is descriptions and titles of commercial products sorted into categories. For exemple, a keyboard with title "large and good keyboard" is in category office > computer > keyboard.

For now, i use "from sklearn.feature\_extraction.text import TfidfVectorizer" for represent my text data. But the matrix is too big in memory.
Do you have any tips for representing a large amount of data?

I was thinking of using word-2-vec to represent the data followed by a neural network for classification.

But I need your advice to get on the right path !!
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Thanks
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