Leveraging Sentiment Analysis Knowledge to Solve Emotion Detection Tasks

Abstract

Identifying and understanding underlying sentiments 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 that leverages knowledge from more simple sentiment analysis tasks to improve the emotion detection task on large scale datasets, such as CMU-MOSEI, using the textual modality only. Results show that our proposed method is competitive with other approaches. Experiments on the CMU-MOSEI, Emotions and GoEmotions datasets show that the knowledge from the sentiment analysis task can indeed be leveraged for emotion recognition.