Pilot results are used to illustrate the potential of an integrated multi-disciplinary approach to semantic text analysis that combines cognitive-oriented human subject experimentation with Machine Learning (ML) based Natural Language Processing (NLP). The goal of this work is to automate the recognition of connotative meaning in text using a range of linguistic and non-linguistic features. This methodology combines human evaluations of text with automated processing of text in order to address social, cognitive and linguistic aspects of connotative meaning. The design of this study places the human in the loop at the point of identifying the degree of connotation present within a given text, rather than at the point of expert linguistic analysis. Findings from the pilot study show that consensus between human subjects regarding the presence of connotative meaning in text can be achieved, that the degree of connotation present in a given text can be described on a continuum, and that a system can be implemented to recognize extreme cases of connotation (or its opposite, denotation). The protocol used in this pilot study was run using a relatively modest number of human evaluators and set of data. Based on the extensibility of methodology and the promising findings described here, the reliability of the data used to train the ML system can be easily improved by increasing the size of both the human subject sample and the data set.