Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel
This addresses emotion analysis for social media users, but it is incremental as it builds on an existing model.
The paper tackled emotion detection on social media by extending the Rule-Based Emission Model to use Plutchik's wheel of emotions, and results showed it is a promising approach that advances the state-of-the-art.
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.