Behavioral sentiment analysis of depressive states

The need to release accurate and incontrovertible diagnoses of depression has fueled the search for new methodologies to obtain more reliable measurements than the commonly adopted questionnaires. In such a context, research has sought to identify non-biased measures derived from analyses of behavioral data such as voice and language. For this purpose, sentiment analysis techniques were developed, initially based on linguistic characteristics extracted from texts and gradually becoming more and more sophisticated by adding tools for the analyses of voice and visual data (such as facial expressions and movements). This work summarizes the behavioral features accounted for detecting depressive states and sentiment analysis tools developed to extract them from text, audio, and video recordings

Cross validation of bi-modal health-related stress assessment

This study explores the feasibility of objective and ubiquitous stress assessment. 25 post-traumatic stress disorder patients participated in a controlled storytelling (ST) study and an ecologically valid reliving (RL) study. The two studies were meant to represent an early and a late therapy session, and each consisted of a "happy" and a "stress triggering" part. Two instruments were chosen to assess the stress level of the patients at various point in time during therapy: (i) speech, used as an objective and ubiquitous stress indicator and (ii) the subjective unit of distress (SUD), a clinically validated Likert scale. In total, 13 statistical parameters were derived from each of five speech features: amplitude, zero-crossings, power, high-frequency power, and pitch. To model the emotional state of the patients, 28 parameters were selected from this set by means of a linear regression model and, subsequently, compressed into 11 principal components. The SUD and speech model were cross-validated, using 3 machine learning algorithms. Between 90% (2 SUD levels) and 39% (10 SUD levels) correct classification was achieved. The two sessions could be discriminated in 89% (for ST) and 77% (for RL) of the cases. This report fills a gap between laboratory and clinical studies, and its results emphasize the usefulness of Computer Aided Diagnostics (CAD) for mental health care

Detecting and Explaining Crisis

Individuals on social media may reveal themselves to be in various states of crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis from social media text automatically and accurately can have profound consequences. However, detecting a general state of crisis without explaining why has limited applications. An explanation in this context is a coherent, concise subset of the text that rationalizes the crisis detection. We explore several methods to detect and explain crisis using a combination of neural and non-neural techniques. We evaluate these techniques on a unique data set obtained from Koko, an anonymous emotional support network available through various messaging applications. We annotate a small subset of the samples labeled with crisis with corresponding explanations. Our best technique significantly outperforms the baseline for detection and explanation.Comment: Accepted at CLPsych, ACL workshop. 8 pages, 5 figure

Imagery rescripting for the treatment of trauma in voice hearers: a case series

Background: High rates of trauma and post-traumatic stress disorder (PTSD) are reported in people who hear voices (auditory hallucinations). A recent metanalysis of trauma interventions in psychosis showed only small improvements in PSTD symptoms and voices. Imagery Rescripting (ImRs) may be a therapy that is more effective in this population because it generalizes over memories, which is ideal in this population with typically repeated traumas. The primary aims of this study were to investigate whether ImR reduces (1) PTSD symptoms and (2) voice frequency and distress in voice hearers. Methods: A single arm open trial study, case-series design. Twelve voice hearers with previous traumas that were thematically related to their voices participated. Brief weekly assessments (administered sessions 1-8, post-intervention, and 3-month follow-up) and longer measures (administered pre-, mid-, and post-intervention) were administered. Mixed regression analysis was used to analyze the results. Results: There was one treatment dropout. Results of the weekly measure showed significant linear reductions over time in all three primary variables - Voice Distress, Voice Frequency, and Trauma Intrusions - all with large effect sizes. These effects were maintained (and continued to improve for Trauma Intrusions) at 3-month follow-up. On the full assessment tools, all measures showed improvement over time, with five outcomes showing significant time effects: trauma, voice frequency, voice distress, voice malevolence and stress. Conclusion: The findings of the current study suggest that ImRs for PTSD symptoms is generally well tolerated and can be therapeutically beneficial among individuals who hear voices