Hi everyone,
I am relatively new to RapidMiner and machine learning in general. In the
forum I couldn't find anything that would solve my problem, sorry if I
missed something there.
I want to build a model which classifies the correct party from a given speech. I have a large corpus with
around 20 000 speeches, which are labeled with one of the six parties.
Therefore I made a little pre processing (transform cases, filter stopwords,
stem...) and built a model via split validation (70% train, 30% test). I
tried a few classification methods like Naive Bayes, libSVM and multinomial
Naive Bayes (weka extension).
Only with the normal Naive Bayes i got a result with around 40% accuracy.
For libSVM and multinomial Naive Bayes (MNB) almost all speeches are predicted
to one party.
But in the literature the latter two models are recommended for text
classification, that's why I'm here to ask, if someone could give me an advice
how to implement a better libSVM/MNB. I am also happy about any other helpful
advice.
Thanks
Jan
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