I am trying to implement text classification and sentiment analysis from the documents.
I always use POS tags as features in the following way.
Mike is playing football
I would convert it into this format: Word_POS
Mike_Noun is_Verb playing_Verb football_Noun
I wanted to know what are the ways I can use NER as features. One of the ways I use is by taking count of NERs as Features. So my sentence would be
Mike_Noun is_Verb playing_Verb football_Noun 0 0
0 is the number of ORG-organisations entities and another
0 is the number of e.g., DATE entities.
So I have 2 questions:
What are the other ways we can use POS tags and NERs as features in
- Without deep learning?
- With deep learning
Also it would be really helpful if you could share resources where I can learn more about feature engineering for Text Data.