I have been given the task to classify some tweets per topic. I have done a classification based only on the per-document-per-topic probability with LDA. I have been suggested to use BTM instead, so I am working on that too. The dataset I work on is made of 3 columns:
- the text,
- the username, and
- the day and hour the post was uploaded.
The problem is that during every classification I made or attempted, I only used the text column. I have been told I should use all three, but I have no idea about how to implement this, and I can't see why usernames should be relevant either for a topic classification. I need to mention that I do not have ground truth, so all techniques I can use are not supervised.
I would be extremely thankful if anyone could share his ideas with me or suggest a path to follow. Thank you in advance.