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Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

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Sentiment Analysis of Suicide Notes: A Shared Task.

John P Pestian¹, Pawel Matykiewicz, Michelle Linn-Gust

¹Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati OH.

Biomedical Informatics Insights

|March 16, 2012

Summary

This summary is machine-generated.

This study developed a new method for assigning emotions to suicide notes using a large, anonymized clinical text corpus. Systems achieved human-like performance, advancing automated emotion analysis in mental health research.

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Published on: May 15, 2016

Area of Science:

Computational Linguistics
Clinical Psychology
Digital Humanities

Background:

Automated emotion assignment from clinical text is challenging.
Previous shared tasks lacked large, anonymized suicide note corpora.
Limited label sets in prior tasks restricted nuanced emotion analysis.

Purpose of the Study:

To conduct a shared task on emotion assignment to suicide notes.
To create and release a permanently available corpus of anonymized clinical text and annotated suicide notes.
To evaluate system performance using a large set of emotion labels.

Main Methods:

Data production involved creating an anonymized corpus of suicide notes.
Annotation required categorization across a broad spectrum of emotion labels.
Evaluation measures were established to assess system performance.

Main Results:

The task facilitated a large number of participants, exceeding previous biomedical challenges.
Systems demonstrated performance levels close to inter-coder agreement.
The developed corpus is now available for future research.

Conclusions:

Automated emotion assignment to suicide notes is feasible with current technology.
The created resource supports further investigation into clinical text analysis.
Human-like performance in this domain is an achievable goal.