Linguistic markers of moderate and absolute natural language

Predicting Personality and Psychological Distress Using Natural Language Processing: A Study Protocol

Background

Self-report multiple choice questionnaires have been widely utilized to quantitatively measure one's personality and psychological constructs. Despite several strengths (e.g., brevity and utility), self-report multiple choice questionnaires have considerable limitations in nature. With the rise of machine learning (ML) and Natural language processing (NLP), researchers in the field of psychology are widely adopting NLP to assess psychological construct to predict human behaviors. However, there is a lack of connections between the work being performed in computer science and that of psychology due to small data sets and unvalidated modeling practices.

Aims

The current article introduces the study method and procedure of phase II which includes the interview questions for the five-factor model (FFM) of personality developed in phase I. This study aims to develop the interview (semi-structured) and open-ended questions for the FFM-based personality assessments, specifically designed with experts in the field of clinical and personality psychology (phase 1), and to collect the personality-related text data using the interview questions and self-report measures on personality and psychological distress (phase 2). The purpose of the study includes examining the relationship between natural language data obtained from the interview questions, measuring the FFM personality constructs, and psychological distress to demonstrate the validity of the natural language-based personality prediction.

Methods

Phase I (pilot) study was conducted to fifty-nine native Korean adults to acquire the personality-related text data from the interview (semi-structured) and open-ended questions based on the FFM of personality. The interview questions were revised and finalized with the feedback from the external expert committee, consisting of personality and clinical psychologists. Based on the established interview questions, a total of 300 Korean adults will be recruited using a convenience sampling method via online survey. The text data collected from interviews will be analyzed using the natural language processing. The results of the online survey including demographic data, depression, anxiety, and personality inventories will be analyzed together in the model to predict individuals' FFM of personality and the level of psychological distress (phase 2).

How Are Curiosity and Interest Different? Naïve Bayes Classification of People’s Beliefs

Researchers studying curiosity and interest note a lack of consensus in whether and how these important motivations for learning are distinct. Empirical attempts to distinguish them are impeded by this lack of conceptual clarity. Following a recent proposal that curiosity and interest are folk concepts, we sought to determine a non-expert consensus view on their distinction using machine learning methods. In Study 1, we demonstrate that there is a consensus in how they are distinguished, by training a Naïve Bayes classification algorithm to distinguish between free-text definitions of curiosity and interest (n = 396 definitions) and using cross-validation to test the classifier on two sets of data (main n = 196; additional n = 218). In Study 2, we demonstrate that the non-expert consensus is shared by experts and can plausibly underscore future empirical work, as the classifier accurately distinguished definitions provided by experts who study curiosity and interest (n = 92). Our results suggest a shared consensus on the distinction between curiosity and interest, providing a basis for much-needed conceptual clarity facilitating future empirical work. This consensus distinguishes curiosity as more active information seeking directed towards specific and previously unknown information. In contrast, interest is more pleasurable, in-depth, less momentary information seeking towards information in domains where people already have knowledge. However, we note that there are similarities between the concepts, as they are both motivating, involve feelings of wanting, and relate to knowledge acquisition.

Reliability in Extreme Isolation: A Natural Language Processing Tool for Stress Self-assessment

Life in isolation is a condition that affects more people every day. From isolation of people in case of an epidemic (Ebola, Coronavirus, ..) to mineworkers or scientist in the Antarctic environment, it is currently well established that extreme isolation is a stressor element which negatively affects human creating stress and social conflict. Living in outer space is one of the most extreme forms of isolation that can be used as a test bench for isolation research. Crews in space are expected to be able to manage independently stress and social conflict problems to be highly reliable and autonomous. Human natural language (NL) has the potential of being a valuable source of information on crew-members stress and reliability in isolation. Based on the use of NL, the PSI (Performance and Stress in Isolation) research group is developing an Artificial Intelligent tool for the autonomous monitoring of stress and reliability. In order to develop this tool, the first step (here presented) is to investigate the correlation of NL with stress and reliability within the PSI experiment. The experiment consisted of three tasks to be performed periodically during the isolation: 1. Analysis of free written communication (NL); 2. Short self-reference questionnaire; and 3. Detection of heart rate and blood pressure values. In this paper we present the preliminary data collected from 29 participants (17 males and 12 females) to 6 space analogue missions accomplished in Europe, the USA and Israel in the 2016 – 2019 time frames. That is, so far, the largest database ever analyzed of this kind. The results are coherent with what is described in the literature about NLP content and style analysis, with the novelty of the correlations found with the cardiovascular parameters, thus reinforcing the perspective of applying a NLP AI system for supporting stress management in extreme isolation contexts from Space to Earth spin off.