A Case Study of Computing Appraisals in Design Text

This paper presents case studies of the calculation of appraisals, the linguistic construal of emotions and attitudinal positions, in natural language design text. Using two data sets, one a standard set of movie review text and one a set of a natural language design text, we compare the performance of support vector machines for the classification of design documents by overall semantic orientation based on two different numerical representations of the design text. For the natural language design text data set, we additionally compare the performance of the support vector machine for the categorization of the text into three categories, Product, Process and People. We find that the sparse yet high dimensional representation of the design text allows the support vector machine to perform best. Further, we find modest benefit in encoding statistically derived data about the semantic orientation and lexical data about the semantic category into the representation beyond frequency counts on the occurrence of unigrams in the text.