  To understand historical texts, we must be aware that language -- including
the emotional connotation attached to words -- changes over time. In this
paper, we aim at estimating the emotion which is associated with a given word
in former language stages of English and German. Emotion is represented
following the popular Valence-Arousal-Dominance (VAD) annotation scheme. While
being more expressive than polarity alone, existing word emotion induction
methods are typically not suited for addressing it. To overcome this
limitation, we present adaptations of two popular algorithms to VAD. To measure
their effectiveness in diachronic settings, we present the first gold standard
for historical word emotions, which was created by scholars with proficiency in
the respective language stages and covers both English and German. In contrast
to claims in previous work, our findings indicate that hand-selecting small
sets of seed words with supposedly stable emotional meaning is actually harmful
rather than helpful.
