Neural Feature Extraction for Contextual Emotion Detection
This paper describes a new approach for the task of contextual emotion detection. The approach is based on a neural feature extractor, composed of a recurrent neural network with an attention mechanism, followed by a classifier, that can be neural or SVM-based. We evaluated the model with the dataset of the task 3 of SemEval 2019 (EmoContext), which includes short 3-turn conversations, tagged with 4 emotion classes. The best performing setup was achieved using ELMo word embeddings and POS tags as input, bidirectional GRU as hidden units, and an SVM as the final classifier. This configuration reached 69.93{\%} in terms of micro-average F1 score on the main 3 emotion classes, a score that outperformed the baseline system by 11.25{\%}.
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Datasets
EmoContext
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Emotion Recognition in Conversation | EC | GRU + SVM | Micro-F1 | 0.6993 | # 9 |
