Question

I am trying to train a Naive Bayes classifier with positive/negative words extracting from a sentiment. example:

I love this movie :))

I hate when it rains 🙁

The idea is I extract positive or negative sentences based on the emoctions used, but in order to train a classifier and persist it into database.

The problem is that I have more than 1 million such sentences, so if I train it word by word, the database will go for a toss. I want to remove all non-relevant word example ‘I’,’this’, ‘when’, ‘it’ so that number of times I have to make a database query is less.

Please help me in resolving this issue to suggest me better ways of doing it

Thank you

Answer 1

You might want to check this out
http://books.google.com/books?id=CE1QzecoVf4C&lpg=PA390&ots=OHuYwLRhag&dq=sentiment%20%20mining%20for%20fortune%20500&pg=PA379#v=onepage&q=sentiment%20%20mining%20for%20fortune%20500&f=false