Text classification for data with multiple labels per observation I have a dataset of tweets that has been labeled by multiple people. So the columns look something like:

Tweet_ID, Coder_1_Classification, Coder_2_Classification, etc.

The idea is to build a tweet classifier based on the labels. How should I input this data into the classifier? I was thinking of taking the plurality response, but is there a way I can use all of the data?

Also, there is partial coverage, so not every labeler labeled every tweet.
There are different options:


Multi-class classification: each tweet can be labelled with several classes
Majority voting: each tweet is labelled with the class chosen most often
Consider tweets with (too many) different labels as ambiguous and remove them from the dataset
...


Additionally it's usually useful to calculate the inter-annotator agreement, because this indicates how subjective the task is and therefore how accurate one can expect the predictions to be.