machine learning - How to output resultant documents from Weka text-classification -


Then we are running a multifunctional naive classification algorithm on a set of 15k tweets. We first broke every tweet in the vector of the Word feature based on the workstringword vector function. After this we can save the results to a new ARF file such as our training set. We repeat this process with the second set of 5k tweets and re-evaluate the test set using the model obtained from our training set.

What we would like to do is to output each classification which is categorized into the test set with classification ... we can see general general information about the performance (Precision, Recall, F-score) and the algorithm The accuracy of but we can classify different sentences on the basis of our reality, which is based on our classifier ... is there any way to do this?

Another problem is that in the end our professor will give us 20k more tweets and hope that we classify this new document. We are not sure how to do this: 

  We are working with all the data that has been manually classified, both training and test sets ... although the data we will be professor Meeting will be un-classified ... how can we re-evaluate our models on unclassified data if the VICA is expected that the set to be used as a specialty information model and Should be the same as the basis of the test, which we are evaluating?   
 Thanks for any help!   
 
 
 The simplest way to do this work is to use a  filtered classifier  are doing. This type of classifier integrates a  filter  and  classifier , so that the classifier you prefer ( J48 ,  Nikebyes , whatever), and you will always keep the original training set (non-existent text), and  StringToWordVector  filters by applying new tweets (unprocessed) to classifier.

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