I'm working on a task which is gender identification. Given a user account (e.g. Twitter account) with its documents (e.g. 100 tweets), the user should be classified as a male or a female.
The dataset that I have contains around 500 users for each class (label).
There are 2 different ways to approach this task, but I don't know which one is "more correct":
1) I aggregate the documents of each user into one large document, and then feed the final document into a classifier.
2) I project the user label (class) into her/his documents, and then feed each single document of the user into a classifier. At the prediction time, I apply averaging on the probabiltities of the users' documents to get the labels of the users (e.g. larger or smaller than 0.5).
Probably an answer to this question could be that both ways are task-dependent, but I want to know if there is a scientific explanation behind any of the solutions, especially in my task.
BTW, some of the documents for many users are not important (e.g.
Hello all :D!).