Context as a Signal: Context-Aware Transformers for Fine-Grained Depression Emotions

Abstract

People who write about depression on social media often convey several emotions that shift within a single post, yet most datasets flatten these dynamics into one post-level label. To enable sentence-level study, we present DepressionEmo-SL, a corpus of Reddit posts in which each sentence is annotated with one of nine emotions: anger, cognitive dysfunction, emptiness, hopelessness, loneliness, sadness, suicide intent, worthlessness, or no emotion. We propose a four-level context protocol (C0-C3) that provides models with progressively larger windows of surrounding text to assess the impact of narrative context on classification. We evaluate five Transformer encoders, including MentalBERT and MentalRoBERTa, under each context setting. Results show that wider narrative context consistently improves performance and that ensembling across context levels provides further improvements. However, these improvements are not uniform across categories, with some emotions benefiting more from additional context than others. Overall, our findings show that context is an important factor in sentence-level depression emotion analysis, while also highlighting the challenges that remain for reliable category-level classification.