The approach shows reasonable performance at individual emotion label prediction, for both emoticons and hashtags.
one of the challenges this poses is that of data annotation: given very large amounts of data, often consisting of very short texts, written in unconventional style and without accompanying metadata, audio/video signals or access to the author for disambiguation, how can we easily produce a gold-standard labelling for training and/or for evaluation and test?