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Proceedings ArticleDOI

Intelligent AI Assisted Psychological Disorder Analysis Using Sentiment Inference

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TLDR
The main aim of the system is to provide a standardized platform for an assistance in diagnosis using Natural Language Processing techniques and Sentiment Inference and suggest to the psychiatrist, the psychological disorder identified and its severity from the document text.
Abstract
In the current scenario, there is no computing system in practice which understands or provides analytical insights for the language used in the psychological documents maintained by psychiatrists. Most importantly, there is no standardized language or methodology used in psychiatric practice to diagnose a psychological disorder. The main aim of our system is to provide a standardized platform for an assistance in diagnosis using Natural Language Processing techniques and Sentiment Inference. The system would suggest to the psychiatrist, the psychological disorder identified and its severity from the document text. The severity is obtained via a gradient score, which is computed by the semantic based gradient processing module - the core of our research. The psychiatrist can approve/correct the system diagnosis if necessary and reassign the gradient values for phrases contributing towards disorder severity. In an automated process, model gets re-trained after a particular threshold is reached, continually improving its performance.

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Citations
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Living With Schizophrenia

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A review on sentiment analysis in psychomedical diagnosis

TL;DR: In this paper, the authors used sentiment analysis (SA) for psychomedical diagnosis of teenagers' behaviour patterns, which can be used for predictive modelling in medical science, huge data is available, but very little work has been done in clinical SA.
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Clinician’s Guide to Bipolar Disorder: Integrating Pharmacology and Psychotherapy

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References
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Journal ArticleDOI

WordNet: a lexical database for English

TL;DR: WordNet1 provides a more effective combination of traditional lexicographic information and modern computing, and is an online lexical database designed for use under program control.
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Steven Bird, Ewan Klein and Edward Loper: Natural Language Processing with Python, Analyzing Text with the Natural Language Toolkit

TL;DR: The book is a practical guide to NLP achieving a balance between NLP theory and practical programming skills, and alternates between focusing on natural language, supported by pertinent programming examples, or focusing on the Python programming language while linguistic examples play a supporting role.
Journal ArticleDOI

Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health.

TL;DR: This paper developed a set of discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes, and found actionable conversation strategies that are associated with better conversation outcomes by applying techniques such as sequence-based conversation models, language model comparisons, message clustering, and psycholinguistics-inspired word frequency analyses.
Journal ArticleDOI

Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project

TL;DR: This work demonstrates the possibility of automatically extracting a broad range of SMI symptoms from English text discharge summaries for patients with an SMI diagnosis and demonstrates the secondary use of mental healthcare data in research.
Posted Content

Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health

TL;DR: This article developed a set of discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes, and found actionable conversation strategies that are associated with better conversation outcomes by applying techniques such as sequence-based conversation models, language model comparisons, message clustering, and psycholinguistics-inspired word frequency analyses.
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