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

Erik Tromp; Mykola Pechenizkiy

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arXiv · 1412.4682

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

Erik Tromp·Mykola 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.

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BibTeX RIS

Erik Tromp, Mykola Pechenizkiy. 2014-12-15. Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel. https://arxiv.org/abs/1412.4682

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