SPLAENet claims state-of-the-art stance detection on RumourEval, SemEval, and P-Stance, but the reported 'average gains' are over the mean of all baselines, not the best baseline.
Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel
1 Pith paper cite this work, alongside 1 external citations. Polarity classification is still indexing.
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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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Emotion-aware Dual Cross-Attentive Neural Network with Label Fusion for Stance Detection in Misinformative Social Media Content
SPLAENet claims state-of-the-art stance detection on RumourEval, SemEval, and P-Stance, but the reported 'average gains' are over the mean of all baselines, not the best baseline.