Identifying and understanding underlying sentiment or emotions in text is a key component of multiple natural language processing applications. While simple polarity sentiment analysis is a well-studied subject, fewer advances have been made in identifying more complex, finer-grained emotions using only textual data. In this paper, we present a Transformer-based model with a Fusion of Adapter layers which leverages knowledge from more simple sentiment analysis tasks to improve the emotion detection task on large scale dataset, such as CMU-MOSEI, using the textual modality only. Results show that our proposed method is competitive with other approaches. We obtained state-of-the-art results for emotion recognition on CMU-MOSEI even while using only the textual modality.
@article{arxiv.2111.03715,
title = {Leveraging Sentiment Analysis Knowledge to Solve Emotion Detection Tasks},
author = {Maude Nguyen-The and Guillaume-Alexandre Bilodeau and Jan Rockemann},
journal= {arXiv preprint arXiv:2111.03715},
year = {2021}
}