Utterance Emotion Dynamics in Children’s Poems: Emotional Changes Across Age (2023)

Abstract: Emerging psychopathology studies are showing that patterns of changes in emotional state — emotion dynamics — are associated with overall well-being and mental health. More recently, there has been some work in tracking emotion dynamics through one’s utterances, allowing for data to be collected on a larger scale across time and people. However, several questions about how emotion dynamics change with age, especially in children, and when determined through children’s writing, remain unanswered. In this work, we use both a lexicon and a machine learning based approach to quantify characteristics of emotion dynamics determined from poems written by children of various ages. We show that both approaches point to similar trends: consistent increasing intensities for some emotions (e.g., anger, fear, joy, sadness, arousal, and dominance) with age and a consistent decreasing valence with age. We also find increasing emotional variability, rise rates (i.e., emotional reactivity), and recovery rates (i.e., emotional regulation) with age. These results act as a useful baselines for further research in how patterns of emotions expressed by children change with age, and their association with mental health.

## introduction emotions play a key role in overall well-being @xcite @xcite ). people's emotional states are constantly changing in response to internal and external events, and the way in which we regulate emotions @xcite . patterns of emotion change over time have been shown to be related to general well-being and psychopathology (the scientific study of mental illness and disorders) @xcite @xcite , academic success @xcite , and social interactions in children @xcite . several psychopathology studies have introduced metrics to quantify and understand the trajectories and patterns in emotions across time @xcite . these metrics are referred to as emotion dynamics and include features of the emotional episode (e.g., duration) and of the emotional trajectory (e.g., emotional variability, covariation, inertia) @xcite . in psychology, emotion dynamics have usually been captured through self-report surveys over periods of time (e.g., five times a day for ten days). however, obtaining such self-reports is arduous work; limiting the amount of data collected. further, selfreports are prone to a number of biases (e.g., social pressures to be perceived as being happy). inspired by the emotion dynamics work in psychology, @xcite recently introduced the idea that patterns of emotion change can also be explored in the utterances of an individual, which can reflect their inner emotion dynamics. they refer to this as utterance emotion dynamics (ued). they generate emotion arcs from streams of text (e.g., sentences in a story, tweets over time, etc.), which are in turn used to determine various ued metrics. 1 different ued metrics capture different aspects of emotion change (e.g., variability, rate of change, etc.). @xcite performed experiments on 36 diverse datasets to show that the quality of emotion arcs generated using emotion lexicons is comparable to those generated from machine learning (ml) methods. the lexicon approach is able to perform well through the power of aggregating information (e.g., 50-100 instances per bin). moreover, the lexicon approach obtains high performance even when using translations of an english lexicon into low-resource languages, such as indigenous african languages @xcite . emotion lexicons have the benefit of interpretability, accessibility, and efficiency compared to ml models. thus, we primarily used a lexicon-based approach in our experiments. however, we also show that the use of ml models points to the same trends as discovered by the lexicon approach. 2 ued metrics, calculated from emotion arcs, can be computed for a speaker over time (e.g., main character in a narrative, tweets of a user over time), for multiple speakers at a time (e.g., treating all users in a geographic region as a speaker for whom we can compute ued), or at an instance level (e.g., independent posts where we compute ued metrics per post). while emotion dynamics have been studied in psychology for the past decades, ued was proposed only recently and has been applied to only a small number of domains (literature and tweets). important questions such as how do ued metrics change over development from toddlers to young adults? and how do the metrics change across one's adult life?, remain unanswered. generally, children's writing is a less studied domain in nlp, and there is limited data available. also, research regarding children has guidelines and regulations in place to protect this vulnerable section of society @xcite ). yet, careful and responsible work such as the work done on the child language data exchange system (childes) (macwhinney, 2014) for understanding child language acquisition can be tremendously influential. similarly, applying ued metrics to children's writing will allow us to infer the emotional states of children across age. such work provides important information for psychologists and child development specialists, as emotion dynamics have been shown to underlie well-being, psychopathology, and success. poetry is a domain of growing interest in nlp (e.g., poem generation (van de cruys, 2020; gonçalo oliveira, 2017)). poems are written to evoke emotions @xcite and a medium through which emotions are expressed @xcite . the intersection of poems and children's writing is an unexplored area which has the potential to unlock patterns in emotion word usage by children as they age. in this paper we contribute to the knowledge of emotion change over time as children age by studying poems written by children. our goal is to apply existing nlp techniques to study emotion change in childrens' writing rather than developing a novel algorithm for better detecting emotion. we investigate the following questions: ## related work below we review related work on emotion dynamics and its ties to well-being, the ued framework, and previous work on children's texts. ## poem datasets for our experiments, we used a dataset of poems written by children as well as a dataset of poems written by adults (as control). the average emotional patterns for emotion dimensions (valence, arousal, and dominance), along with discrete emotions (anger, fear, joy and sadness) were analysed across grades. additionally, these patterns were contrasted to those found in poems written by adults (data described below). it was found that as children grow from early childhood into adolescence, valence decreases reaching a minimum at grade 11. whereas arousal increases with age, aligning with how adults display emotions more visibly @xcite . likewise, dominance increases with age. consistently there was higher arousal in poems written by children with names commonly among males compared to those with names common among females. all intensities for anger, fear, joy and sadness increased across grades, with a particularly strong increase in sadness. poems written by adults (fpp): hipson and mohammad (2020) also compiled and used poems written by adults which were published on the famous poets and poems website. 4 we will refer to this dataset as fpp. the poems are publicly available online and contain works by famous writers such as edgar allan poe, and e.e. cummings. preprocessing: we preprocessed both poem datasets by removing extra whitespace, punctuation, unescaping html (if any), tokenizing and lowercasing the text using the twokenize foot_5 library. additionally, stop words were removed. ## types of ued metrics in the past, ued metrics have been calculated for the speaker or jointly for text from a set of speakers (meta-speaker). we propose a third form of ued metrics not explored before -instance level ued metrics. all three of these types of ued metrics are described below: in the past, speaker ued metrics have been determined for characters in movie dialogues @xcite , and for users on twitter during the pandemic @xcite . in this work we are interested in children's poems (instances of poetic text) across age and not how each individual child has a different writing style. therefore, we calculate ued metrics for each poem and average the scores for each grade. ## experiments our goal is to analyze how patterns of emotion words change with age in children's poems. in order to do so, we generate an emotion arc per poem and compute instance-level ued metrics. afterwards, we average the ued metrics per grade to compare results across age. we use the emotion dynamics toolkit foot_6 to calculate ued metrics and our code for the experiments is available online. 7 we use text windows of size five (excluding words with a neutral emotion score) and a step size of one to create an emotion arc per poem. we only considered poems that included at least five emotion words 8 . for each research question we computed the corresponding metrics: average emotional state, emotional variability, rise rate and recovery rate. analyzing average emotion and variability allows us to build foundational knowledge into changes in patterns of emotion words. we then look at rise rate and recovery rate to further our understanding of children's emotion dynamics. while older children (e.g., grade 10-12) tend to write on average longer poems than younger children (e.g., grade 1-3), these ued metrics are not affected by the length of the poems. 9 other metrics calculate the number of displacements from home base or the length of displacements to peaks which are affected by poem length. additionally, because poems are shorter than text streams such as novels, the number of windows that can be created from a poem is limited, so metrics specific to emotional displacement are not computed since they are more suitable for longer texts. each metric is computed for both dimensional emotions (e.g., valence, arousal, dominance) and discrete emotions (e.g., anger, fear, joy and sadness). we used the nrc valence, arousal, and dominance (vad) lexicon @xcite and the nrc emotion intensity lexicon (mohammad, 2018b) for word-emotion scores. in section 5.1 we use the lexicon-based approach to generate emotion arcs. we explain how the metrics are computed, contrast the trends across grades and compare the results to poems written by adults. we discuss the ties of these results with work in psychology and implications for emotional development. in section 5.2 we explore the same questions using an ml model for generating emotion arcs. we find similar trends across grade with the ml approach as when using the lexicon approach. ued metric valence arousal dominance anger fear joy sadness psych. construct average 0.228 -0.247 -0.087 0.018 0.028 0.040 0.025 intensity variability 0.219 0.182 0.167 0.031 0.043 0.048 0.038 emotional variability rise rate 0.134 0.114 0.084 0.115 0.109 0.066 0.113 emotional reactivity recovery rate 0.127 0.105 0.086 0.024 0.028 0.023 0.020 emotional regulation ## conclusion we explored four utterance emotion dynamics metrics (average, variability, rise rate, and recovery rate), and seven emotions (three dimensional and four discrete) on poems written by children and adults. we found that the patterns of emotion changes in poetry by children supported previous results and findings in the psychology literature (e.g., increased variability, rise rate, and recovery rates with age). as future work, we would like to examine poetry by adults more in-depth, such as how do patterns of emotion change look for experts vs. novices? and how do ued compare across geographic regions, and time periods.