DeepEmo: Learning and Enriching Pattern-Based Emotion Representations
This work addresses emotion analysis in online text, offering incremental improvements for applications like social media monitoring.
The authors tackled emotion recognition from online text by proposing a graph-based method to extract and enrich emotion-bearing patterns, which outperformed most state-of-the-art techniques in experiments.
We propose a graph-based mechanism to extract rich-emotion bearing patterns, which fosters a deeper analysis of online emotional expressions, from a corpus. The patterns are then enriched with word embeddings and evaluated through several emotion recognition tasks. Moreover, we conduct analysis on the emotion-oriented patterns to demonstrate its applicability and to explore its properties. Our experimental results demonstrate that the proposed techniques outperform most state-of-the-art emotion recognition techniques.