I am approaching machine learning for the first time because of my studies. I have been given a bunch of tweets and the goal is to classify them per topic. I really have no clue on how this should be done. Is there a particular way to follow?
Until now, I have only found topics and was thinking about making a DTM-like dataframe for the training data containing not only the number of times not-sparse words occur but also the number of times particular N-grams occur and a ground truth column with the topic.
Is this totally wrong? How else could I train a classifier without having features?