Dear all,
I am using Naive Bayes classifier for my sentiment analysis on
customer support. But unfortunately I don't have huge annotated data sets
in the customer support domain. But I have a little amount of annotated
data in the same domain(around 100 positive and 100 negative). And I have
the amazon product review data set as well.
Is there anyway can I implement a weighted naive bayes
classifier using mahout as mentioned in
this<http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CFIQFjAE&url=http%3A%2F%2Fwww.cs.waikato.ac.nz%2F~eibe%2Fpubs%2FUAI_200.pdf&ei=7YvwTprnI8rMrQelmJzWDw&usg=AFQjCNFqWxTYVxqSLTH8ULZvAPqyvDHhXw&sig2=jxLnRoUZhK5OVQ1KCCGj9A>,
so that I can give more weight to the small set of customer support data
and small weight to the amazon product review data. A training on the above
weighted data set would drastically improve accuracy I guess. Kindly help
me with the same.
--
With Thanks and Regards,
Ramprakash Ramamoorthy,
B.Tech ICT,
SASTRA University.
+91 9626975420
