“splink” is happy and “phrouth” is scary: Emotion Intensity Analysis for Nonsense Words (2022)

Abstract: 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”).

## introduction with words come meanings, as well as a variety of associations such as emotional nuances. emotions, feelings, and attitudes, which can be summarized under the umbrella term of "affect", are in fact a core component for the meaning of large portions of a language vocabulary @xcite . in english, they encompass nouns, verbs, adjectives, and adverbs @xcite . for instance, dejected and wistful can be said to directly express an emotion, but there are also terms that do not describe a state of emotion and are still associated to one (e.g., failure and death foot_0 ), given an interpretation of an associated event. most computational studies of emotions in text deal with words in context, for instance in news headlines @xcite or in tweets @xcite @xcite . analyzing words in isolation, however, is equally important, as it can help to create lexical resources for use in applications @xcite @xcite , to investigate how words are processed in general @xcite , part 2), and more specifically, to obtain a better understanding of first language acquisition processes @xcite . when considering words in isolation, their meaning cannot be disambiguated by the surrounding text. this raises the question: can readers interpret an emotional load from unknown words, which are judged out of their context? we address this question by analyzing emotion associations of "nonsense" words -or nonwords, or pseudowords, i.e., terms which resemble real entries in the english vocabulary, but are actually not part of it @xcite . our aim is to understand the degree to which nonsense words like fonk, knunk, or snusp can be associated to particular emotions. we model the problem as an emotion intensity analysis task with a set of basic emotions, namely fear, anger, joy, disgust, surprise, and sadness. other fields have provided evidence that some phonemes can be related to the affective dimension of valence @xcite , but emotion analysis, and in particular word-based research, has not yet ventured this direction. gaining insight on the emotional tone of non-existing expressions could be relevant for current computational emotion classification and intensity regression efforts, which have manifold applications across social media mining or digital humanities. as an example, when new product names are coined which do not have an established semantics, designers and marketing experts might want to be aware of the potential affective connections that these evoke, and avoid those with a negative impact. therefore, our main contributions are: (1) the creation of an emotion intensity lexicon of 272 nonsense words (with in addition 68 real words, for comparison to previous work), (2) the analysis of the phonemes present in them (if pronounced as english words) that aligns with emotion intensity studies across the ekman (1999) basic emotions, and ( 3 ) experiments in which we develop intensity regressors on a large resource of real words, as well as on our nonsense words. both regressors are evaluated on real and nonsense words. ## data acquisition and annotation we now describe the creation of our corpus of nonsense and real words, with their respective emotion intensity scores for the six emotions of joy, sadness, anger, fear, disgust, and surprise. 2 we show an excerpt of our data in appendix b. for each quadruple, decide which of the four words you associate most with joy and which you associate least with joy. which of the four words do you associate most and which do you associate least with joy? least most knoice janc scrarsh boil least most groose throaf sulb which of the four words do you associate most and which do you associate least with joy? yurch ## corpus analysis we now discuss the reliability of the annotation process and then analyze the resulting resource. ## modeling our analysis has revealed that particular phonemes are indeed related to high intensities for some emotions. in the following section, we aim at understanding if these findings are exploited by computational models that perform emotion intensity regression (i.e., if these models perform better when they observe specific character sequences or phoneme sequences), and if a model that is trained on real words can generalize the learned emotion associations to nonsense words (or the other way around). ## conclusion & future work we addressed the question of whether humans associate emotion intensities with nonsense words and tested if machine learning-based regressors pick up phonetic patterns to make emotion intensity predictions. our annotation study revealed that humans do indeed make such associations. especially the first phoneme of a word influences the resulting emotion intensity judgement: /p/ and /s/ seem to increase the perception of joy, /sh/ of surprise, and /m/ is more likely related to sadness. contrary to our assumptions, phonemes placed at the last position of a nonword also play an important role. the phoneme /p/, for instance, points towards an increased degree of disgust. we found that our emotion intensity regressors do predict emotion intensity based on word form and pronunciation, although only to a limited degree for nonsense words. training on nonsense items and testing on real vocabulary entries results in a low performance, thus indicating that the meaning of known words overrules patterns that can be deduced from nonsense ones. when learned the other way around, our computational models make use of patterns found in real words that, to some degree, allow the emotion intensity prediction on nonsense counterparts. one limitation of this first study of written nonsense words and their emotion association is the comparably limited size of the corpus we compiled. future work could perform the annotation study with more items and across more diverse sets of annotators. furthermore, our analysis focused on single phonemes that we selected based on their frequency in the data. this way of selecting the phonemes under investigation neglects the dependence between their frequencies and their positions. it also disregards potential interactions between different phonemes, as well as the role of less frequent phonemes in emotion intensity decisions. future work should take into account these types of considerations.