Leveraging Sentiment Analysis Knowledge to Solve Emotion Detection Tasks

• URL: http://arxiv.org/abs/2111.03715v1
• Date: Fri, 5 Nov 2021 20:06:58 GMT
• Title: Leveraging Sentiment Analysis Knowledge to Solve Emotion Detection Tasks
• Authors: Maude Nguyen-The, Guillaume-Alexandre Bilodeau and Jan Rockemann
• Abstract summary: We present a Transformer-based model with a Fusion of Adapter layers to improve the emotion detection task on large scale dataset. We obtained state-of-the-art results for emotion recognition on CMU-MOSEI even while using only the textual modality.
• Score: 11.928873764689458
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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