In the case of large-scale epidemiological studies, there is evidence of substantial disagreement when lay diagnoses of schizophrenia based on structured interviews are compared with expert diagnoses of the same patients. Reasons for this level of disagreement are investigated in the current study, which made use of advances in text-mining techniques and associated structural representations of language expressions. Specifically, the current study examined whether content analyses of transcribed diagnostic interviews obtained from 150 persons with serious psychiatric disorders yielded any discernable patterns that correlated with diagnostic inconsistencies of schizophrenia. In summary, it was found that the patterning or structure of spontaneous self-reports of emotion states in the diagnostic interview was associated with diagnostic inconsistencies of schizophrenia, irrespective of confounders; i.e., age of patient, gender, or ethnicity. In particular, complex emotion patterns were associated with greater disagreement between experts and trained lay interviewers than were simpler patterns.
"For example, Chung and Pennebaker (2008) identified dimensions of people thinking about themselves by developing an automated meaning extraction method for natural language . Gara et al. (2010) investigated the reasons for disagreement in schizophrenia diagnosis by mining the textual transcripts of structured interviews. "
[Show abstract][Hide abstract] ABSTRACT: Much evidence has shown that people's physical and mental health can be predicted by the words they use. However, such verbal information is seldom used in the screening and diagnosis process probably because the procedure to handle these words is rather difficult with traditional quantitative methods. The first challenge would be to extract robust information from diversified expression patterns, the second to transform unstructured text into a structuralized dataset. The present study developed a new textual assessment method to screen the posttraumatic stress disorder (PTSD) patients using lexical features in the self narratives with text mining techniques. Using 300 self narratives collected online, we extracted highly discriminative keywords with the Chi-square algorithm and constructed a textual assessment model to classify individuals with the presence or absence of PTSD. This resulted in a high agreement between computer and psychiatrists' diagnoses for PTSD and revealed some expressive characteristics in the writings of PTSD patients. Although the results of text analysis are not completely analogous to the results of structured interviews in PTSD diagnosis, the application of text mining is a promising addition to assessing PTSD in clinical and research settings.
Psychiatry Research 03/2012; 198(3). DOI:10.1016/j.psychres.2012.01.032 · 2.47 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Emotion’s twin roles–‘unite and divide’ our daily life, thus motivating the good and the worst in human behaviour. The way
one “feels” does influence the way one “acts” toward others. If this reasoning is correct, then behaviour can never be without
motive or “motiveless.” Given the importance of emotions in human communication and decision making, the context of intergroup
relations, with its themes of prejudice, stereotyping and discrimination against culturally dissimilar was touched on selectively.
Using the Differential Emotions Scale and building upon Boyle’s (Personality 56:747–750, 1984) work, with students, the present study reports a repeated-measure multiple discriminant function analysis for items across
raters. The findings further indicate that most of the DES items are sensitive indicators. The correspondence of some of the
results with prior research findings makes facial emotions less the holy grail of the social behaviour field. Likely consequences
of emotions are considered, and research needs are discussed vis-a-vis uplifting individual happiness, collective identity
and sense of connection to others. Perhaps its implications can be extended to the literature, thus revealing how the different
lenses through which human emotions are usually viewed are connected by the incipient/concept attitude and self-identification/labelling
that run through each of them.
Social Indicators Research 07/2012; 107(3):1-30. DOI:10.1007/s11205-011-9862-1 · 1.40 Impact Factor
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