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