how to classify text based on more than one column
In order to make the model take into account the two columns and distinguish whether a word is from the title or the article, you can generate the two sets of features independently and then concatenate the two vectors of features. This way the learning algorithm can assign different weights to a particular word depending on whether it comes from the title or the article.
However there is a disadvantage in doing that: depending on how many instances, how many words etc. there are, increasing the number of features might make the task harder for the model (for instance cause overfitting). It's probably worth testing the two options:

concatenate the text from the two columns then generate the features, i.e. no distinction between the columns but simpler job for the classifier
generate the features independently then concatenate the two sets of features (as above).