Deep Representations of First-person Pronouns for Prediction of
  Depression Symptom Severity
Xinyang Ren Hannah A Burkhardt Patricia A Areán Thomas D Hull Trevor Cohen
Prior work has shown that analyzing the use of first-person singular pronouns
can provide insight into individuals' mental status, especially depression
symptom severity. These findings were generated by counting frequencies of
first-person singular pronouns in text data. However, counting doesn't capture
how these pronouns are used. Recent advances in neural language modeling have
leveraged methods generating contextual embeddings. In this study, we sought to
utilize the embeddings of first-person pronouns obtained from contextualized
language representation models to capture ways these pronouns are used, to
analyze mental status. De-identified text messages sent during online
psychotherapy with weekly assessment of depression severity were used for
evaluation. Results indicate the advantage of contextualized first-person
pronoun embeddings over standard classification token embeddings and
frequency-based pronoun analysis results in predicting depression symptom
severity. This suggests contextual representations of first-person pronouns can
enhance the predictive utility of language used by people with depression
symptoms.