I have a corpus of text with a corresponding topics. For example
"A rapper Tupac was shot in LA" and it was labelled as
["celebrity", "murder"]. So basically each vector of features can have many labels (not the same amount. The first feature vector can have 3 labels, second 1, third 5).
If I would have just one label corresponded to each text, I would try a Naive Bayes classifier, but I do not really know how should I proceed if I can have many labels.
Is there any way to transform Naive Bayes into multi label classification problem (if there is a better approach - please let me know)?
P.S. few things about the data I have.
- approximately 10.000 elements in the dataset
- text is approximately 2-3 sentences
- maximum 7 labels per text