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 and evaluate them on real and nonsense words, and across these categories (making use of the NRC emotion intensity lexicon of 7493 words). The data analysis 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").

研究了人们是如何从语音和词素等表层模式出发，分配词语情感强度值的，并发现人们不仅根据表面模式，而且根据词语间的相似性，如“juy”与“joy”或“flike”与“like”，将情感意义赋予词语。

splink”是愉快的，“phrouth”是可怕的：无意义词汇的情感强度分析