Rule-based emotion detection on social media : putting tweets on Plutchik's wheel

E. Tromp, M. Pechenizkiy

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Abstract

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.
Original languageEnglish
Publishers.n.
Number of pages6
Publication statusPublished - 2014

Publication series

NamearXiv
Volume1412.4682 [cs.CL]

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