  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.
