Classify tweets by topic 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?
Since there are no predefined topics, the task is unsupervised: the goal is to group tweets which are semantically similar together (as opposed to classification, which requires training a model to predict among specific classes).
The standard unsupervised approach is topic modelling. In the traditional LDA approach, a topic model groups the documents into clusters and also provides the probability of a word given a topic, so a list of &quot;top words&quot; by topic can be extracted from the model. LDA requires the number of topics as input parameter but Hierarchical Dirichlet Processes can be used to avoid this issue (it's less common however).