Modeling Word Emotion in Historical Language: Quantity Beats Supposed Stability in Seed Word Selection
This addresses the challenge of understanding emotional changes in historical texts for linguists and historians, but it is incremental as it adapts existing methods to a new annotation scheme.
The paper tackled the problem of estimating emotional connotations of words in historical English and German by adapting existing algorithms to the Valence-Arousal-Dominance scheme, finding that using small hand-selected seed words with supposedly stable emotions is harmful rather than helpful.
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.