Emotion analysis (EA) and sentiment analysis are closely related tasks differing in the psychological phenomenon they aim to catch. We address fine-grained models for EA which treat the computation of the emotional status of narrative documents as a regression rather than a classification problem, as performed by coarse-grained approaches. We introduce Ekman's Basic Emotions (BE) and Russell and Mehrabian's Valence-Arousal-Dominance (VAD) model—two major schemes of emotion representation following opposing lines of psychological research, i.e., categorical and dimensional models—and discuss problems when BEs are used in a regression approach. We present the first natural language system thoroughly evaluated for fine-grained emotion analysis using the VAD scheme. Although we only employ simple BOW features, we reach correlation values up until r = .65 with human annotations. Furthermore, we show that the prevailing evaluation methodology relying solely on Pearson's correlation coefficient r is deficient which leads us to the introduction of a complementary error-based metric. Due to the lack of comparable (VAD-based) systems, we, finally, introduce a novel method of mapping between VAD and BE emotion representations to create a reasonable basis for comparison. This enables us to evaluate VAD output against human BE judgments and, thus, allows for a more direct comparison with existing BE-based emotion analysis systems. Even with this, admittedly, error-prone transformation step our VAD-based system achieves state-of-the-art performance in three out of six emotion categories, out-performing all existing BE-based systems but one.