How to use binary text classifier(built using SVM with TF-IDF) to classify new text document? 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?

Lets take an example

Training Set:
Doc_1: Chelsea won the match. {Sports}
Doc_2: India won the third test match against Austrailia {Sports}
Doc_3: I want to sleep {Non-Sport}
Doc_4: 13 palace to see in Auckland {Non-Sport}

New Testing Set:
Doc_5: Climate change impacts in Austrailia


Now how can I find IDF score of "Austrailia" in Doc_5 without merging this document with training set?

Since Doc_5 contains the word "Austrailia", it will change the IDF score of "Australia" in Doc_1 will also change, thus model needs retraining
What is your model built in? 

Most popular libraries have a score function separate from the training part. You should be able to just pass the new document to the score function of the trained model and get back the predicted class.