Information contributed by messages depends on a context and the situation. We analyze scalar, comparative, and degree information and their language signals. We use the signals to represent messages in automated assessment of their informativeness. We employ Machine Learning methods to study relations between informativeness and product evaluations. Consumer reviews provide data for experiments. The obtained results have many possible applications in sentiment analysis, information extraction, and collaborative filtering.