Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features

In recent years, there has been increased in-terest in building predictive models that har-ness natural language processing and machine learning techniques to detect emotions from various text sources, including social media posts, micro-blogs or news articles. Yet, deployment of such models in real-world sentiment and emotion applications faces challenges, in particular poor out-of-domain generalizability. This is likely due to domain-speciﬁc differences (e.g., topics, communica-tive goals, and annotation schemes) that make transfer between different models of emotion recognition difﬁcult. In this work we propose approaches for text-based emotion detection that leverage transformer models (BERT and RoBERTa) in combination with Bidirectional Long Short-Term Memory (BiLSTM) networks trained on a comprehensive set of psycholinguistic features. First, we evaluate the performance of our models within-domain on two benchmark datasets: GoEmotion (Dem-szky et al., 2020) and ISEAR (Scherer and Wallbott, 1994). Second, we conduct transfer learning experiments on six datasets from the Uniﬁed Emotion Dataset (Bostan and Klinger, 2018) to evaluate their out-of-domain robust-ness. We ﬁnd that the proposed hybrid models improve the ability to generalize to out-of-distribution data compared to a standard transformer-based approach. Moreover, we observe that these models perform competitively on in-domain data.