Measurement of negativity bias in personal narratives using corpus-based emotion dictionaries.

This study presents a novel methodology for the measurement of negativity bias using positive and negative dictionaries of emotion words applied to autobiographical narratives. At odds with the cognitive theory of mood dysregulation, previous text-analytical studies have failed to find significant correlation between emotion dictionaries and negative affectivity or dysphoria. In the present study, an a priori list dictionary of emotion words was refined based on the actual use of these words in personal narratives collected from close to 500 college students. Half of the corpus was used to construct, via concordance analysis, the grammatical structures associated with the words in their emotional sense. The second half of the corpus served as a validation corpus. The resulting dictionary ignores words that are not used in their intended emotional sense, including negated emotions, homophones, frozen idioms etc. Correlations of the resulting corpus-based negative and positive emotion dictionaries with self-report measures of negative affectivity were in the expected direction, and were statistically significant, with medium effect size. The potential use of these dictionaries as implicit measures of negativity bias and in the analysis of psychotherapy transcripts is discussed.