We examine if common machine learning techniques known to perform well in coarsegrained emotion and sentiment classification can also be applied successfully on a set of fine-grained emotion categories. We first describe the grounded theory approach used to develop a corpus of 5,553 tweets manually annotated with 28 emotion categories. From our preliminary experiments, we have identified two machine learning algorithms that perform well in this emotion classification task and demonstrated that it is feasible to train classifiers to detect 28 emotion categories without a huge drop in performance compared to coarser-grained classification schemes.
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