Watkins & Rush, 1983; Peterson, 1992; Cacioppo et al., 1997; Davison et al., 1997; Hurlburt, 1997; Hartman-Hall & Haaga, 1999) content analysis of spontaneous speech has been criticized for being training-intensive and susceptible to rater drift and other rater-based biases (e.g. Hurlburt, 1997; Pennebaker et al., 2003). In contrast, computerized text-analysis can process large amounts of transcribed speech in considerably shorter time (Gottschalk & Bechtel, 1982; Pennebaker et al., 2003; Alpers et al., 2005). Moreover, several studies that used computerized text-analysis of multiple categories of words have demonstrated superiority of this technique over human raters in assessing mental health (Rosenberg et al., 1990; Pennebaker et al., 1997; Pennebaker & King, 1999). This study reports the construction and initial validation of an index of negativity bias based on context-based dictionaries for positive and negative emotional language. These dictionaries can be plugged into text-analytical software to obtain a metric representing the positive and negative emotional content of the speech sample. The construction process is described briefly, while a more detailed account is now under review. The advantage of these dictionaries, as well as the importance of contextualized dictionary entries for the computerized detection of emotional language are then discussed. Method General Overview: The construction of both positive and negative dictionaries followed several phases. In 1, three research assistants independently detected candidates for emotion words in
Watkins & Rush, 1983; Peterson, 1992; Cacioppo et al., 1997; Davison et al., 1997; Hurlburt, 1997; Hartman-Hall & Haaga, 1999) content analysis of spontaneous speech has been criticized for being training-intensive and susceptible to rater drift and other rater-based biases (e.g. Hurlburt, 1997; Pennebaker et al., 2003). In contrast, computerized text-analysis can process large amounts of transcribed speech in considerably shorter time (Gottschalk & Bechtel, 1982; Pennebaker et al., 2003; Alpers et al., 2005). Moreover, several studies that used computerized text-analysis of multiple categories of words have demonstrated superiority of this technique over human raters in assessing mental health (Rosenberg et al., 1990; Pennebaker et al., 1997; Pennebaker & King, 1999). This study reports the construction and initial validation of an index of negativity bias based on context-based dictionaries for positive and negative emotional language. These dictionaries can be plugged into text-analytical software to obtain a metric representing the positive and negative emotional content of the speech sample. The construction process is described briefly, while a more detailed account is now under review. The advantage of these dictionaries, as well as the importance of contextualized dictionary entries for the computerized detection of emotional language are then discussed. Method General Overview: The construction of both positive and negative dictionaries followed several phases. In 1, three research assistants independently detected candidates for emotion words in
shukicohen_CognitiveBias021909_Rev.docx