> > >> Or Do I have flexibility to give some other input specific to my problem ? >> Such as if words like "Problem", "Complaint" etc are more likely to appear >> in a text containing grievance. >> >> >>
> You can provide a Weight, usually TF-IDF, that often does a good job of >> factoring in the importance of words. If you have certain sentiment words >> that you think influence things one way or the other, you could consider a >> weighting process that adds weight to those words, I suppose, but I would >> want to experiment with that a bit. >> > > I would first get your data in the bayes format <LABEL><TAB><FEATURE1><SPACE><FEATURE2>...... Feature can be words, or pairs of word (word1_word2) or binned numerical values ( 0.1, 0.2.. etc) or enums. (SEX:MALE, SEX:FEMALE) Give this as input to the classifier and get the output. If you need to add couple words hardcoded into the classifier. Add them as a training instance. Since features are assumed to be independent in bayes. it doesnt matter how you give them POS<TAB>problem<TAB>complaint<TAB>problemo > > > On Thu, Sep 30, 2010 at 8:55 PM, Robin Anil <[email protected]> wrote: > >> It does that by default for all words. What else do you have in mind? >> >> On Thu, Sep 30, 2010 at 8:07 PM, Neil Ghosh <[email protected]> wrote: >> >>> Does anybody have examples/reference how to use TF-IDF weights in mahout >>> cbayes for particular words and phrases while doing text classification ? >>> >>> -- >>> Thanks and Regards >>> Neil >>> http://neilghosh.com >>> >> >> > > > -- > Thanks and Regards > Neil > http://neilghosh.com > > > >
