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

TL;DR

This paper presents RBEM-Emo, a rule-based method for detecting complex emotions in social media texts using Plutchik's wheel, outperforming existing techniques in accuracy.

Contribution

The paper introduces RBEM-Emo, a novel rule-based algorithm for emotion detection based on Plutchik's model, extending previous sentiment analysis approaches.

Findings

RBEM-Emo outperforms state-of-the-art emotion detection methods.

The approach effectively captures nuanced emotions beyond polarity.

Experimental results show promising accuracy improvements.

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.