@inproceedings{allen-etal-2019-convsent,
    title = "{C}onv{S}ent at {CLP}sych 2019 Task A: Using Post-level Sentiment Features for Suicide Risk Prediction on {R}eddit",
    author = "Allen, Kristen  and
      Bagroy, Shrey  and
      Davis, Alex  and
      Krishnamurti, Tamar",
    editor = "Niederhoffer, Kate  and
      Hollingshead, Kristy  and
      Resnik, Philip  and
      Resnik, Rebecca  and
      Loveys, Kate",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-3024/",
    doi = "10.18653/v1/W19-3024",
    pages = "182--187",
    abstract = "This work aims to infer mental health status from public text for early detection of suicide risk. It contributes to Shared Task A in the 2019 CLPsych workshop by predicting users' suicide risk given posts in the Reddit subforum r/SuicideWatch. We use a convolutional neural network to incorporate LIWC information at the Reddit post level about topics discussed, first-person focus, emotional experience, grammatical choices, and thematic style. In sorting users into one of four risk categories, our best system`s macro-averaged F1 score was 0.50 on the withheld test set. The work demonstrates the predictive power of the Linguistic Inquiry and Word Count dictionary, in conjunction with a convolutional network and holistic consideration of each post and user."
}