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I have built binary text classifier using SVM on TF-IDF for news articles(Sports: Non-Sports).

But I not sure how to classify new document using this model. Since TF-IDF is calculated based on the occurrence of a word in all other documents.

Do I have merge test and train data every time I receive a new document for classification? It will change the model as well every time.

Am I missing something? I think, although SVM on TF-IDF giving good results it can not be used in production.

Is there any other way to tackle this issue?

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The TF-IDF weights are normally locked in using just the training data. They are not updated later as you run your classifier on new documents. They are there as a form of data normalization to help the classifier learn the best model possible, changing them after the model is trained doesn't make sense.

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