  People associate affective meanings to words - "death" is scary and sad while
"party" is connotated with surprise and joy. This raises the question if the
association is purely a product of the learned affective imports inherent to
semantic meanings, or is also an effect of other features of words, e.g.,
morphological and phonological patterns. We approach this question with an
annotation-based analysis leveraging nonsense words. Specifically, we conduct a
best-worst scaling crowdsourcing study in which participants assign intensity
scores for joy, sadness, anger, disgust, fear, and surprise to 272 non-sense
words and, for comparison of the results to previous work, to 68 real words.
Based on this resource, we develop character-level and phonology-based
intensity regressors. We evaluate them on both nonsense words and real words
(making use of the NRC emotion intensity lexicon of 7493 words), across six
emotion categories. The analysis of our data reveals that some phonetic
patterns show clear differences between emotion intensities. For instance, s as
a first phoneme contributes to joy, sh to surprise, p as last phoneme more to
disgust than to anger and fear. In the modelling experiments, a regressor
trained on real words from the NRC emotion intensity lexicon shows a higher
performance (r = 0.17) than regressors that aim at learning the emotion
connotation purely from nonsense words. We conclude that humans do associate
affective meaning to words based on surface patterns, but also based on
similarities to existing words ("juy" to "joy", or "flike" to "like").
