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We study sentiment analysis beyond the typical granularity of polarity andinstead use Plutchik's wheel of emotions model. We introduce RBEM-Emo as anextension to the Rule-Based Emission Model algorithm to deduce such emotionsfrom human-written messages. We evaluate our approach on two different datasetsand compare its performance with the current state-of-the-art techniques foremotion detection, including a recursive auto-encoder. The results of theexperimental study suggest that RBEM-Emo is a promising approach advancing thecurrent state-of-the-art in emotion detection.

Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel