Deep into that darkness peering: A computational analysis of the role of depression in Edgar Allan Poe's life and death

Dean, Hannah J.; Boyd, Ryan L.

doi:10.1016/j.jad.2020.01.098

Cited by 8 publications

(7 citation statements)

References 45 publications

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“…Moreover, language has been used to monitor specific psychological disorders. This has consistently been demonstrated with depression (e.g., Dean & Boyd, 2020;Schwartz et al, 2014;Park & Conway, 2017), whereby changes in depressive states have been successfully measured through language. Relatedly, changes in suicidal ideation can also be precisely detected and measured through language (e.g., De Choudhury et al, 2016;Ma-Kellams et al, 2016).…”

Section: Lesson 2: Language Can Assist With Measuring and Tracking Psychopathology Over Timementioning

confidence: 67%

Personality Disorder and Verbal Behavior

Entwistle¹,

Marceau²,

Boyd³

2021

Preprint

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Today, the development of new technologies means that there are many advanced tools that can be used to improve our understanding of personality disorder, and, in turn, the treatment of personality disorder. One particularly promising tool — indeed, the focus of this chapter — is computerized language analysis. Through the exploration and analysis of verbal behavior, it is possible to empirically develop new insights into personality disorder, broadly defined. That is, by looking at patterns in the way that people with personality disorder use language — the words that they use and the way in which they use them — we can gain access into their broad constellation of thinking, feelings, and behaviors, as well as how precisely each of these features contributes to their pathology.To date, however, there has been very little research at the intersection of verbal behavior and personality pathology. Accordingly, the goal of this chapter is to describe and discuss how personality disorder may become better understood through the application of natural language analysis, providing a rough roadmap for the development of personality disorder studies using modern methods. Specifically, in this chapter we will provide:1. A brief background and overview of personality disorder;2. An overview of how natural language processing (NLP) methods have advanced understanding within the wider field of psychology, focusing on personality psychology and psychopathology specifically; 3. Examples that demonstrate how NLP methods can help to resolve some of the fundamental, unanswered questions and debates in the personality disorder literature.

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Section: Lesson 2: Language Can Assist With Measuring and Tracking Psychopathology Over Timementioning

confidence: 67%

Personality Disorder and Verbal Behavior

Entwistle¹,

Marceau²,

Boyd³

2021

Preprint

Self Cite

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“…These findings are significant from two perspectives. On one hand, they lend evidence to the point that sentiment polarities and emotions are one of the key features for the recognition of psychological states (Desmet and Hoste, 2013;Dean and Boyd, 2020). Some possible reasons are as follows.…”

Section: Discussionmentioning

confidence: 97%

Linguistic features and psychological states: A machine-learning based approach

Sun²

2022

Front. Psychol.

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Previous research mostly used simplistic measures and limited linguistic features (e.g., personal pronouns, absolutist words, and sentiment words) in a text to identify its author’s psychological states. In this study, we proposed using additional linguistic features, that is, sentiments polarities and emotions, to classify texts of various psychological states. A large dataset of forum posts including texts of anxiety, depression, suicide ideation, and normal states were experimented with machine-learning algorithms. The results showed that the proposed linguistic features with machine-learning algorithms, namely Support Vector Machine and Deep Learning achieved a high level of performance in the detection of psychological state. The study represents one of the first attempts that uses sentiment polarities and emotions to detect texts of psychological states, and the findings may contribute to our understanding of how accuracy may be enhanced in the detection of various psychological states. Significance and suggestions of the study are also offered.

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“…As an illustrative example, we can consider the approach of Dean & Boyd (2020), which used existing knowledge about the markers of depression (e.g., negative affect, self-focus, etc.) in order to inform a computational tool for estimating depression from text.…”

Section: Developing and Refining Word Setsmentioning

confidence: 99%

Text Analysis for Psychology: Methods, Principles, and Practices

Kennedy¹,

Ashokkumar²,

Boyd³

et al. 2021

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Due to the explosion of new sources of human language data and the rapid progression of computational methods for extracting meaning from natural language, language analysis is a promising, though complicated, category of psychological research. In this chapter, we give a modern perspective on language analysis as it applies to psychology, uniting historical context, the diverse range of domains studied in psychology via language, and the methodological rigor of natural language processing (NLP) and machine learning. Top–down methods (e.g., dictionary approaches, text annotation) are presented alongside bottom–up methods (e.g., topic modeling, word embedding, language modeling) in order to give the reader a comprehensive grounding in the tools available and the recommended practices involved in applying them. We conclude with a view of the future of language analysis, specifically the ways in which psychology and NLP will continue to co-develop.

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Deep into that darkness peering: A computational analysis of the role of depression in Edgar Allan Poe's life and death

Cited by 8 publications

References 45 publications

Personality Disorder and Verbal Behavior

Personality Disorder and Verbal Behavior

Linguistic features and psychological states: A machine-learning based approach

Text Analysis for Psychology: Methods, Principles, and Practices

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