Traditional sentiment analysis methods use lexicons or machine learning models to classify text as positive or negative. These approaches are unable to capture nuance or intensity in short or informal texts. We propose a novel method that uses the Self-Assessment Manikin (SAM) valence scale, which provides a continuous measurement of sentiment, ranging from extremely positive to extremely negative. We describe the development of a lexicon of emotion-laden words with SAM valence scales and investigate its application to fine-grained sentiment analysis. We also propose a lexicon-based polarity approach to complement textual features in machine learning models trained to predict a numerical sentiment label for a given text. This method is evaluated using a new dataset of short texts with sentiment labels based on expert ratings, which are predicted using various machine learning fusion mechanisms. The lexicon-based polarity method is found to provide improvements of 0.250, 0.999, and 0.261 in the mean squared error for classical machine learning, RNN, and transformer-based architectures, respectively. © 2025 by the authors.