Hi Peter,
number of samples: 1 million tweets
number of features: I use the bag of words model, in-fact I have followed this
example
http://scikit-learn.github.com/scikit-learn-tutorial/working_with_text_data.html.
It uses TF-IDF normalization.
class distribution: equal number of positive and negative tweets
features: I removed the stop words, punctuations, URLs and user names,
Adnan
________________________________
From: Peter Prettenhofer <[email protected]>
To: [email protected]
Sent: Thursday, February 2, 2012 2:20 PM
Subject: Re: [Scikit-learn-general] Improving the accuracy of classifier
Hi Adnan,
can you give use some more specific information about your learning
task / dataset including:
- number of samples
- number of features
- class distribution
- features (normalization, preprocessing)
best,
Peter
2012/2/2 adnan rajper <[email protected]>:
> hi everybody,
>
> I am using multinomial and LinearSVC classifier with default parameters to
> classify twitter messages into two classes (positive or negative). I
> followed the tutorial
> on http://scikit-learn.github.com/scikit-learn-tutorial/working_with_text_data.html.
> I tried "parameter tuning using grid search", but it gets too slow. Both
> classifiers (multinomial and LinearSVC) give 75% accuracy. My problem is
> that I want to improve the accuracy, for instance I want to make it more
> than 80%. Is there anyway to do it through scikit.
>
>
> thanks
> Adnan
>
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