The food industry is seeking ways to understand consumer emotions, using implicit measurements, to differentiate acceptability of products in the marketplace. Automated facial expression analysis (AFEA) is a prospective analysis for product acceptability. This study used aqueous bitter solutions to determine and validate AFEA as an analysis supplement to product liking. Participants (n = 46) evaluated a control (distilled water) and three bitter (caffeine) solutions: low (0.05% w/v); medium (0.08% w/v); and high (0.15% w/v). Individual participant sessions were video-recorded and analyzed (5 s; α = 0.20) for each sample in the default and continuous analysis setting. Participants rated liking and bitter intensity on a 9-point scale. An inverse relationship existed between liking and bitter intensity (rs = −0.90; p < 0.0001). In continuous setting for AFEA analysis of mean emotion intensity, analyzed by ANOVA, only the medium bitter treatment elicited a higher disgust response control (p < 0.20) and no differences were found between treatments in disgust (p > 0.20) evaluations using program default settings. For time series analysis with both the continuous and default settings, disgust was a predominant emotion in the medium and high bitter solutions as well as happy in the high (p < 0.025). Using time series analysis, continuous and default results had similar patterns over 5 s, but continuous data was more intermittent. Time series analysis is a promising tool for interpreting emotional results of a population and is more sensitive to emotional changes than mean comparisons. Future studies should continue to improve the characterization and sensitivity of emotions to food acceptability using AFEA.