New answers tagged tfidf
1
urllib parse seems like the function for you. With this you are able to extract keywords from the net location and the path separately if you desire to process them separately or even if you want to join them back again later.
The result should look something like this:
from urllib.parse import urlparse
o = urlparse('https://www.forbes.com/sites/...
0
Using bigrams and trigrams is likely to generate a high number of features, but with a small dataset the traditional approach would be to reduce the number of features. You could start by removing the least frequent words/n-grams (e.g. less than 3 occurrences), and/or use feature selection with InfoGain. It might not be very accurate but at least you avoid ...
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Check my answer to this question. Nowadays there're many pretrained embedders to choose from. They'll give you fixed-size numerical vector of features. You don't even have to go DNN way, xgboost will work just fine.
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