An active learning based emoji prediction method in Turkish

Abstract

Emoji usage has become a standard in social media platforms since it can condense feelings beyond short textual information. Recent advances in machine learning enable to write short messages with automatically detected emojis. However, the prediction of emojis for the given short message can be complicated, inasmuch as users can interpret different meanings beyond the intent of their designers. Therefore, an automatic extraction strategy of training samples cannot be convenient from the large volumes of unlabelled tweets. In this paper, we present an active learning method to evaluate the emoji prediction of a tweet with a limited number of labelled Turkish emoji dataset. To simulate a human-machine collaborative learning method, we train an initial classifier with this dataset and then we update the classifier by filtering related samples out from the large pool of unlabelled data. In the evaluation, we split 25% randomly selected tweets combined with only one emoji from the generated dataset as a test case. Our active learning method has achieved 0.901 F1 score and outperforms other baseline supervised learning methods.