# Modeling user context for valence prediction from narratives

## TL;DR

This paper explores how modeling multiple personal narratives can improve the prediction of a person's emotional valence, emphasizing the importance of context and textual information for mental health insights.

## Contribution

It introduces a method for using multiple narratives per individual to enhance valence prediction, focusing on textual data for better generalizability.

## Key findings

- Models capturing inter-individual differences improve prediction accuracy.
- Contextual information from multiple narratives enhances emotional state prediction.
- Text-based models are effective despite limited audio data availability.

## Abstract

Automated prediction of valence, one key feature of a person's emotional state, from individuals' personal narratives may provide crucial information for mental healthcare (e.g. early diagnosis of mental diseases, supervision of disease course, etc.). In the Interspeech 2018 ComParE Self-Assessed Affect challenge, the task of valence prediction was framed as a three-class classification problem using 8 seconds fragments from individuals' narratives. As such, the task did not allow for exploring contextual information of the narratives. In this work, we investigate the intrinsic information from multiple narratives recounted by the same individual in order to predict their current state-of-mind. Furthermore, with generalizability in mind, we decided to focus our experiments exclusively on textual information as the public availability of audio narratives is limited compared to text. Our hypothesis is, that context modeling might provide insights about emotion triggering concepts (e.g. events, people, places) mentioned in the narratives that are linked to an individual's state of mind. We explore multiple machine learning techniques to model narratives. We find that the models are able to capture inter-individual differences, leading to more accurate predictions of an individual's emotional state, as compared to single narratives.