Editorial on the Research Topic Semantic Algorithms in the Assessment of Attitudes and Personalit

Arnulf, Jan Ketil

Larson, Kai R.

Martinsen, Oyvind Lund

Nimon, Kim F.

Frontiers in Psychology

PubMed

The methodological tools available for psychological and organizational assessment are rapidly advancing through natural language processing (NLP). Computerized analyses of texts are increasingly available as extensions of traditional psychometric approaches. The present Research Topic is recognizing the contributions but also the challenges in publishing such inter-disciplinary research. We therefore sought to provide an open-access avenue for cutting-edge research to introduce and illustrate the various applications of semantics in the assessment of attitudes and personality. The result is a collection of empirical contributions spanning from assessment of psychological states through methodological biases to construct identity detection.
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Martinsen, Øyvind Lund

Nimon, Kim F

Larsen, Kai R.

CU Scholar Institutional Repository

EDITORIALpublished: 23 July 2021doi: 10.3389/fpsyg.2021.720559Frontiers in Psychology | www.frontiersin.org 1 July 2021 | Volume 12 | Article 720559Edited and reviewed by:Giovanni Pilato,Institute for High PerformanceComputing and Networking(ICAR), Italy*Correspondence:Jan Ketil Arnulfjan.k.arnulf@bi.noSpecialty section:This article was submitted toPersonality and Social Psychology,a section of the journalFrontiers in PsychologyReceived: 04 June 2021Accepted: 28 June 2021Published: 23 July 2021Citation:Arnulf JK, Larsen KR, Martinsen ØLand Nimon KF (2021) Editorial:Semantic Algorithms in theAssessment of Attitudes andPersonality.Front. Psychol. 12:720559.doi: 10.3389/fpsyg.2021.720559Editorial: Semantic Algorithms in theAssessment of Attitudes andPersonalityJan Ketil Arnulf 1*, Kai R. Larsen 2, Øyvind Lund Martinsen 1 and Kim F. Nimon 31 BI Norwegian Business School, Oslo, Norway, 2 Leeds School of Business, University of Colorado, Boulder, CO,United States, 3 Soules College of Business, The University of Texas at Tyler, Tyler, TX, United StatesKeywords: latent semantic analysis, survey research, organizational behavior, voting behavior, trust, motivation,clinical psychology, artificial intelligenceEditorial on the Research TopicSemantic Algorithms in the Assessment of Attitudes and PersonalityThe methodological tools available for psychological and organizational assessment are rapidlyadvancing through natural language processing (NLP). Computerized analyses of texts areincreasingly available as extensions of traditional psychometric approaches. The present ResearchTopic is recognizing the contributions but also the challenges in publishing such inter-disciplinaryresearch. We therefore sought to provide an open-access avenue for cutting-edge research tointroduce and illustrate the various applications of semantics in the assessment of attitudes andpersonality. The result is a collection of empirical contributions spanning from assessment ofpsychological states through methodological biases to construct identity detection.To understand previous research leading up to this issue, one important starting point was theapplication of machine learning to the assessment of attitudes measured by Larsen et al. (2008).Observing how the output from semantic algorithms could identify high correlations among items,Larsen et al. (2008, p. 3) introduced a mechanism to check for language-driven survey results:“Manifest validity is expected to support researchers during the data analysis stage in thatresearchers can compare measures of manifest validity (evaluating the extent of semantic differencebetween different scales) to item correlations computed from actual responses. In cases where thereis little difference between distances proposed by correlation coefficients, the respondents are morelikely to have employed shallow processing during questionnaire analysis.”Since then, researchers have expanded the use of semantic similarity of scale items to exploresurvey responses in a number of ways. Studies have shown that semantics may predict surveyresponses in organizational behavior (Arnulf et al., 2014, 2018c), leadership (Arnulf and Larsen,2015, Arnulf et al., 2018b,d), employee engagement (Nimon et al., 2016), technology acceptance(Gefen and Larsen, 2017), and intrinsic motivation (Arnulf et al., 2018a).In a parallel line of previous research, semantic analysis has been used to complement and extenddata from traditional rating scales (e.g., Nicodemus et al., 2014; Bååth et al., 2019; Garcia et al., 2020,Kjell et al., 2019). Since semantic analysis can detect overlap among items and rating scales, they canbe used to map relationships and overlap between existing or new scales (e.g., Rosenbusch et al.,2020) and even to detect construct identities and ameliorate the jingle/jangle problem in theorybuilding (e.g., Larsen and Bong, 2016).While the salient points of several of the articles presented in this Research Topic weresemantically similar to prior literature, several others were more diverse (see Figure 1).Arnulf et al. Editorial: Semantic Algorithms in Psychological AssessmentFIGURE 1 | 3D-Plot of Research Topic and prior literature abstracts semanticsimilarity. Prior encompasses the literature reviewed in the editorial notincluding the articles contributing to the Research Topic. Darkness of linesrepresents the magnitude of the cosines resulting from conducting LSA on theabstracts in the Research Topic and prior literature.Arnulf and Larsen and Arnulf et al. are arguably mostsimilar to the body of literature previously reviewed. In botharticles, LSA of survey items predicted survey responses tovarying degrees. Arnulf and Larsen’s research questioned thecapability of traditional survey responses to detect culturaldifferences. Observed differences in the semantically drivenpatterns of survey responses from eleven different ethnicsamples appeared to be caused by different translationsand understanding rather than cultural dependencies.Arnulf et al. similarly found that different score levels inprevalent motivation measures among 18 job types could beexplained by differences in semantic patterns between thejob types.Gefen et al. conducted LSA on items sets associated with trustand distrust and found that the resulting distance matrix of theitems yielded a covariance-based structural equation model thatwas consistent with theory.Kjell O. et al. found that open-ended, computational languageassessments of well-being were distinctly related to a theoreticallyrelevant behavioral outcome, whereas data from standard, close-ended numerical rating scales were not. In a similar manner, KjellK. et al. found that freely generated word responses analyzedwith artificial intelligence significantly correlated with individualitems connected to the DSM 5 diagnostic criteria of depressionand anxiety.Chen et al. manually annotated Facebook posts to assesssocial media affect and found that extraverted participantstended to post positive content continuously, more agreeableparticipants tended to avoid posting negative content, andparticipants with stronger depression symptoms posted morenon-original content.Garcia et al. applied LSA to Reuter news and Facebook statusupdates. In the case of the Reuter corpus, the past was devaluatedrelative to both the present and the future and in the case of theFacebook corpus, the past and present were devaluated againstthe future. Based on those findings, the authors concluded thatpeople strive to communicate the promotion of a bright futureand the prevention of a dark future.Fredén and Sikstrom applied LSA to voter descriptions ofleaders and parties and found that descriptions of leaderspredicted vote choice to a similar extent as descriptions of parties.Nimon provided a dataset of documents from Taking theMeasure of Work and demonstrated how it could be used to builda LSA space.As the NLP field continues to develop and mature andthe opportunity to automatically transform open-ended data toquantifiable measures, one wonders to what degree the use ofrating scales will be warranted in the future. Taken together,the applications demonstrated here go a long way in makingfree responses accessible to statistical treatment. Similarly, theNLP approaches even seem to allow statistical help in theorybuilding, as the constructs themselves and their relationshipswith measurement scales may be modeled independently ofresponse data. We invite readers to consider how NLP canadvance and/or potentially replace the use of rating scales in theassessment of personality and attitudes.AUTHOR CONTRIBUTIONSAll authors listed have made a substantial, direct and intellectualcontribution to the work, and approved it for publication.REFERENCESArnulf, J. K., and Larsen, K. R. (2015). Overlapping semantics of leadershipand heroism: expectations of omnipotence, identification with idealleaders and disappointment in real managers. Scand. Psychol. 2:e3.doi: 10.15714/scandpsychol.2.e3Arnulf, J. K., Larsen, K. R., and Dysvik, A. (2018c). Measuring semanticcomponents in training and motivation: a methodological introduction tothe semantic theory of survey response. Hum. Resour. Dev. Q. 30, 17–38.doi: 10.1002/hrdq.21324Arnulf, J. K., Larsen, K. R., and Martinsen, Ø. L. (2018a).Respondent robotics: simulating responses to likert-scalesurvey items. Sage Open 8, 1–18. doi: 10.1177/2158244018764803Arnulf, J. K., Larsen, K. R., and Martinsen, Ø. L. (2018b). Semantic algorithms candetect how media language shapes survey responses in organizationalbehaviour. PLoS ONE 13:e0207643. doi: 10.1371/journal.pone.0207643Arnulf, J. K., Larsen, K. R., Martinsen, O. L., and Bong, C. H.(2014). Predicting survey responses: how and why semantics shapesurvey statistics on organizational behaviour. PLoS ONE 9:e106361.doi: 10.1371/journal.pone.0106361Arnulf, J. K., Larsen, K. R., Martinsen, Ø. L., and Egeland, T. (2018d). The failingmeasurement of attitudes: how semantic determinants of individual surveyFrontiers in Psychology | www.frontiersin.org 2 July 2021 | Volume 12 | Article 720559Arnulf et al. Editorial: Semantic Algorithms in Psychological Assessmentresponses come to replace measures of attitude strength. Behav. Res. Methods50, 2345–2365. doi: 10.3758/s13428-017-0999-yBååth, R., Sikström, S., Kalnak, N., Hansson, K., and Sahlén, B. (2019). Latentsemantic analysis discriminates children with developmental language disorder(DLD) from children with typical language development. J. Psycholinguist. Res.48, 683–697. doi: 10.1007/s10936-018-09625-8Garcia, D., Rosenberg, P., Nima, A. A., Granjard, A., Cloninger, K. M., andSikström, S. (2020). Validation of two short personality inventories using self-descriptions in natural language and quantitative semantics test theory. Front.Psychol. 11:16. doi: 10.3389/fpsyg.2020.00016Gefen, D., and Larsen, K. (2017). Controlling for lexical closeness in surveyresearch: a demonstration on the technology acceptance model. J. Assoc.Inform. Syst. 18, 727–757.doi: 10.17705/1jais.00469Kjell, O. N. E., Kjell, K., Garcia, D., and Sikstrom, S. (2019). Semanticmeasures: using natural language processing to measure, differentiate,and describe psychological constructs. Psychol. Methods 24,92–115. doi: 10.1037/met0000191Larsen, K., Nevo, D., and Rich, E. (2008). “Exploring the semantic validity ofquestionnaire scales,” in Proceedings of the 41st Annual Hawaii InternationalConference on System Sciences (Waikoloa, HI), 1–10.Larsen, K. R., and Bong, C. H. (2016). A tool for addressing constructidentity in literature reviews and meta-analyses. MIS Q. 40, 529–551.doi: 10.25300/Misq/2016/40.3.01Nicodemus, K., Elvevag, B., Foltz, P. W., Rosenstein, M., Diaz-Asper,C., and Weinberger, D. R. (2014). Category fluency, latent semanticanalysis and schizophrenia: a candidate gene approach. Cortex 55,182–191. doi: 10.1016/j.cortex.2013.12.004Nimon, K., Shuck, B., and Zigarmi, D. (2016). Construct overlap betweenemployee engagement and job satisfaction: a function of semanticequivalence? J. Happ. Stud. 17, 1149–1171. doi: 10.1007/s10902-015-9636-6Rosenbusch, H., Wanders, F., and Pit, I. L. (2020). The semantic scale network:an online tool to detect semantic overlap of psychological scales andprevent scale redundancies. Psychol. Methods 25, 380–392. doi: 10.1037/met0000244Conflict of Interest: The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could be construed as apotential conflict of interest.Publisher’s Note: All claims expressed in this article are solely those of the authorsand do not necessarily represent those of their affiliated organizations, or those ofthe publisher, the editors and the reviewers. Any product that may be evaluated inthis article, or claim that may be made by its manufacturer, is not guaranteed orendorsed by the publisher.Copyright © 2021 Arnulf, Larsen, Martinsen and Nimon. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, providedthe original author(s) and the copyright owner(s) are credited and that the originalpublication in this journal is cited, in accordance with accepted academic practice.No use, distribution or reproduction is permitted which does not comply with theseterms.Frontiers in Psychology | www.frontiersin.org 3 July 2021 | Volume 12 | Article 720559

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Editorial: Semantic Algorithms in the Assessment of Attitudes and Personality

Scholar Works at UT Tyler (University of Texas at Tyler)

University of Texas at Tyler Scholar Works at UT Tyler Human Resource Development Faculty Publications and Presentations Human Resource Development 7-23-2021 Editorial: Semantic Algorithms in the Assessment of Attitudes and Personality Jan Ketil Arnulf Kai R. Larson Oyvind Lund Martinsen Kim F. Nimon University of Texas at Tyler, knimon@uttyler.edu Follow this and additional works at: https://scholarworks.uttyler.edu/hrd_fac  Part of the Human Resources Management Commons Recommended Citation Arnulf, Jan Ketil; Larson, Kai R.; Martinsen, Oyvind Lund; and Nimon, Kim F., "Editorial: Semantic Algorithms in the Assessment of Attitudes and Personality" (2021). Human Resource Development Faculty Publications and Presentations. Paper 39. http://hdl.handle.net/10950/4309 This Article is brought to you for free and open access by the Human Resource Development at Scholar Works at UT Tyler. It has been accepted for inclusion in Human Resource Development Faculty Publications and Presentations by an authorized administrator of Scholar Works at UT Tyler. For more information, please contact tgullings@uttyler.edu. EDITORIALpublished: 23 July 2021doi: 10.3389/fpsyg.2021.720559Frontiers in Psychology | www.frontiersin.org 1 July 2021 | Volume 12 | Article 720559Edited and reviewed by:Giovanni Pilato,Institute for High PerformanceComputing and Networking(ICAR), Italy*Correspondence:Jan Ketil Arnulfjan.k.arnulf@bi.noSpecialty section:This article was submitted toPersonality and Social Psychology,a section of the journalFrontiers in PsychologyReceived: 04 June 2021Accepted: 28 June 2021Published: 23 July 2021Citation:Arnulf JK, Larsen KR, Martinsen ØLand Nimon KF (2021) Editorial:Semantic Algorithms in theAssessment of Attitudes andPersonality.Front. Psychol. 12:720559.doi: 10.3389/fpsyg.2021.720559Editorial: Semantic Algorithms in theAssessment of Attitudes andPersonalityJan Ketil Arnulf 1*, Kai R. Larsen 2, Øyvind Lund Martinsen 1 and Kim F. Nimon 31 BI Norwegian Business School, Oslo, Norway, 2 Leeds School of Business, University of Colorado, Boulder, CO,United States, 3 Soules College of Business, The University of Texas at Tyler, Tyler, TX, United StatesKeywords: latent semantic analysis, survey research, organizational behavior, voting behavior, trust, motivation,clinical psychology, artificial intelligenceEditorial on the Research TopicSemantic Algorithms in the Assessment of Attitudes and PersonalityThe methodological tools available for psychological and organizational assessment are rapidlyadvancing through natural language processing (NLP). Computerized analyses of texts areincreasingly available as extensions of traditional psychometric approaches. The present ResearchTopic is recognizing the contributions but also the challenges in publishing such inter-disciplinaryresearch. We therefore sought to provide an open-access avenue for cutting-edge research tointroduce and illustrate the various applications of semantics in the assessment of attitudes andpersonality. The result is a collection of empirical contributions spanning from assessment ofpsychological states through methodological biases to construct identity detection.To understand previous research leading up to this issue, one important starting point was theapplication of machine learning to the assessment of attitudes measured by Larsen et al. (2008).Observing how the output from semantic algorithms could identify high correlations among items,Larsen et al. (2008, p. 3) introduced a mechanism to check for language-driven survey results:“Manifest validity is expected to support researchers during the data analysis stage in thatresearchers can compare measures of manifest validity (evaluating the extent of semantic differencebetween different scales) to item correlations computed from actual responses. In cases where thereis little difference between distances proposed by correlation coefficients, the respondents are morelikely to have employed shallow processing during questionnaire analysis.”Since then, researchers have expanded the use of semantic similarity of scale items to exploresurvey responses in a number of ways. Studies have shown that semantics may predict surveyresponses in organizational behavior (Arnulf et al., 2014, 2018c), leadership (Arnulf and Larsen,2015, Arnulf et al., 2018b,d), employee engagement (Nimon et al., 2016), technology acceptance(Gefen and Larsen, 2017), and intrinsic motivation (Arnulf et al., 2018a).In a parallel line of previous research, semantic analysis has been used to complement and extenddata from traditional rating scales (e.g., Nicodemus et al., 2014; Bååth et al., 2019; Garcia et al., 2020,Kjell et al., 2019). Since semantic analysis can detect overlap among items and rating scales, they canbe used to map relationships and overlap between existing or new scales (e.g., Rosenbusch et al.,2020) and even to detect construct identities and ameliorate the jingle/jangle problem in theorybuilding (e.g., Larsen and Bong, 2016).While the salient points of several of the articles presented in this Research Topic weresemantically similar to prior literature, several others were more diverse (see Figure 1).Arnulf et al. Editorial: Semantic Algorithms in Psychological AssessmentFIGURE 1 | 3D-Plot of Research Topic and prior literature abstracts semanticsimilarity. Prior encompasses the literature reviewed in the editorial notincluding the articles contributing to the Research Topic. Darkness of linesrepresents the magnitude of the cosines resulting from conducting LSA on theabstracts in the Research Topic and prior literature.Arnulf and Larsen and Arnulf et al. are arguably mostsimilar to the body of literature previously reviewed. In botharticles, LSA of survey items predicted survey responses tovarying degrees. Arnulf and Larsen’s research questioned thecapability of traditional survey responses to detect culturaldifferences. Observed differences in the semantically drivenpatterns of survey responses from eleven different ethnicsamples appeared to be caused by different translationsand understanding rather than cultural dependencies.Arnulf et al. similarly found that different score levels inprevalent motivation measures among 18 job types could beexplained by differences in semantic patterns between thejob types.Gefen et al. conducted LSA on items sets associated with trustand distrust and found that the resulting distance matrix of theitems yielded a covariance-based structural equation model thatwas consistent with theory.Kjell O. et al. found that open-ended, computational languageassessments of well-being were distinctly related to a theoreticallyrelevant behavioral outcome, whereas data from standard, close-ended numerical rating scales were not. In a similar manner, KjellK. et al. found that freely generated word responses analyzedwith artificial intelligence significantly correlated with individualitems connected to the DSM 5 diagnostic criteria of depressionand anxiety.Chen et al. manually annotated Facebook posts to assesssocial media affect and found that extraverted participantstended to post positive content continuously, more agreeableparticipants tended to avoid posting negative content, andparticipants with stronger depression symptoms posted morenon-original content.Garcia et al. applied LSA to Reuter news and Facebook statusupdates. In the case of the Reuter corpus, the past was devaluatedrelative to both the present and the future and in the case of theFacebook corpus, the past and present were devaluated againstthe future. Based on those findings, the authors concluded thatpeople strive to communicate the promotion of a bright futureand the prevention of a dark future.Fredén and Sikstrom applied LSA to voter descriptions ofleaders and parties and found that descriptions of leaderspredicted vote choice to a similar extent as descriptions of parties.Nimon provided a dataset of documents from Taking theMeasure of Work and demonstrated how it could be used to builda LSA space.As the NLP field continues to develop and mature andthe opportunity to automatically transform open-ended data toquantifiable measures, one wonders to what degree the use ofrating scales will be warranted in the future. Taken together,the applications demonstrated here go a long way in makingfree responses accessible to statistical treatment. Similarly, theNLP approaches even seem to allow statistical help in theorybuilding, as the constructs themselves and their relationshipswith measurement scales may be modeled independently ofresponse data. We invite readers to consider how NLP canadvance and/or potentially replace the use of rating scales in theassessment of personality and attitudes.AUTHOR CONTRIBUTIONSAll authors listed have made a substantial, direct and intellectualcontribution to the work, and approved it for publication.REFERENCESArnulf, J. K., and Larsen, K. R. (2015). Overlapping semantics of leadershipand heroism: expectations of omnipotence, identification with idealleaders and disappointment in real managers. Scand. Psychol. 2:e3.doi: 10.15714/scandpsychol.2.e3Arnulf, J. K., Larsen, K. R., and Dysvik, A. (2018c). Measuring semanticcomponents in training and motivation: a methodological introduction tothe semantic theory of survey response. Hum. Resour. Dev. Q. 30, 17–38.doi: 10.1002/hrdq.21324Arnulf, J. K., Larsen, K. R., and Martinsen, Ø. L. (2018a).Respondent robotics: simulating responses to likert-scalesurvey items. Sage Open 8, 1–18. doi: 10.1177/2158244018764803Arnulf, J. K., Larsen, K. R., and Martinsen, Ø. L. (2018b). Semantic algorithms candetect how media language shapes survey responses in organizationalbehaviour. PLoS ONE 13:e0207643. doi: 10.1371/journal.pone.0207643Arnulf, J. K., Larsen, K. R., Martinsen, O. L., and Bong, C. H.(2014). Predicting survey responses: how and why semantics shapesurvey statistics on organizational behaviour. PLoS ONE 9:e106361.doi: 10.1371/journal.pone.0106361Arnulf, J. K., Larsen, K. R., Martinsen, Ø. L., and Egeland, T. (2018d). The failingmeasurement of attitudes: how semantic determinants of individual surveyFrontiers in Psychology | www.frontiersin.org 2 July 2021 | Volume 12 | Article 720559Arnulf et al. Editorial: Semantic Algorithms in Psychological Assessmentresponses come to replace measures of attitude strength. Behav. Res. Methods50, 2345–2365. doi: 10.3758/s13428-017-0999-yBååth, R., Sikström, S., Kalnak, N., Hansson, K., and Sahlén, B. (2019). Latentsemantic analysis discriminates children with developmental language disorder(DLD) from children with typical language development. J. Psycholinguist. Res.48, 683–697. doi: 10.1007/s10936-018-09625-8Garcia, D., Rosenberg, P., Nima, A. A., Granjard, A., Cloninger, K. M., andSikström, S. (2020). Validation of two short personality inventories using self-descriptions in natural language and quantitative semantics test theory. Front.Psychol. 11:16. doi: 10.3389/fpsyg.2020.00016Gefen, D., and Larsen, K. (2017). Controlling for lexical closeness in surveyresearch: a demonstration on the technology acceptance model. J. Assoc.Inform. Syst. 18, 727–757.doi: 10.17705/1jais.00469Kjell, O. N. E., Kjell, K., Garcia, D., and Sikstrom, S. (2019). Semanticmeasures: using natural language processing to measure, differentiate,and describe psychological constructs. Psychol. Methods 24,92–115. doi: 10.1037/met0000191Larsen, K., Nevo, D., and Rich, E. (2008). “Exploring the semantic validity ofquestionnaire scales,” in Proceedings of the 41st Annual Hawaii InternationalConference on System Sciences (Waikoloa, HI), 1–10.Larsen, K. R., and Bong, C. H. (2016). A tool for addressing constructidentity in literature reviews and meta-analyses. MIS Q. 40, 529–551.doi: 10.25300/Misq/2016/40.3.01Nicodemus, K., Elvevag, B., Foltz, P. W., Rosenstein, M., Diaz-Asper,C., and Weinberger, D. R. (2014). Category fluency, latent semanticanalysis and schizophrenia: a candidate gene approach. Cortex 55,182–191. doi: 10.1016/j.cortex.2013.12.004Nimon, K., Shuck, B., and Zigarmi, D. (2016). Construct overlap betweenemployee engagement and job satisfaction: a function of semanticequivalence? J. Happ. Stud. 17, 1149–1171. doi: 10.1007/s10902-015-9636-6Rosenbusch, H., Wanders, F., and Pit, I. L. (2020). The semantic scale network:an online tool to detect semantic overlap of psychological scales andprevent scale redundancies. Psychol. Methods 25, 380–392. doi: 10.1037/met0000244Conflict of Interest: The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could be construed as apotential conflict of interest.Publisher’s Note: All claims expressed in this article are solely those of the authorsand do not necessarily represent those of their affiliated organizations, or those ofthe publisher, the editors and the reviewers. Any product that may be evaluated inthis article, or claim that may be made by its manufacturer, is not guaranteed orendorsed by the publisher.Copyright © 2021 Arnulf, Larsen, Martinsen and Nimon. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, providedthe original author(s) and the copyright owner(s) are credited and that the originalpublication in this journal is cited, in accordance with accepted academic practice.No use, distribution or reproduction is permitted which does not comply with theseterms.Frontiers in Psychology | www.frontiersin.org 3 July 2021 | Volume 12 | Article 720559

Editorial: Semantic Algorithms in the Assessment of Attitudes and Personality

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