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.
@article{arxiv.1412.4682,
title = {Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel},
author = {Erik Tromp and Mykola Pechenizkiy},
journal= {arXiv preprint arXiv:1412.4682},
year = {2014}
}