Emotion Dynamics in Movie Dialogues

Emotion dynamics is a framework for measuring how an individual's emotions change over time. It is a powerful tool for understanding how we behave and interact with the world. In this paper, we introduce a framework to track emotion dynamics through one's utterances. Specifically we introduce a number of utterance emotion dynamics (UED) metrics inspired by work in Psychology. We use this approach to trace emotional arcs of movie characters. We analyze thousands of such character arcs to test hypotheses that inform our broader understanding of stories. Notably, we show that there is a tendency for characters to use increasingly more negative words and become increasingly emotionally discordant with each other until about 90 percent of the narrative length. UED also has applications in behavior studies, social sciences, and public health.

SOLO: A Corpus of Tweets for Examining the State of Being Alone

The state of being alone can have a substantial impact on our lives, though experiences with time alone diverge significantly among individuals. Psychologists distinguish between the concept of solitude, a positive state of voluntary aloneness, and the concept of loneliness, a negative state of dissatisfaction with the quality of one's social interactions. Here, for the first time, we conduct a large-scale computational analysis to explore how the terms associated with the state of being alone are used in online language. We present SOLO (State of Being Alone), a corpus of over 4 million tweets collected with query terms 'solitude', 'lonely', and 'loneliness'. We use SOLO to analyze the language and emotions associated with the state of being alone. We show that the term 'solitude' tends to co-occur with more positive, high-dominance words (e.g., enjoy, bliss) while the terms 'lonely' and 'loneliness' frequently co-occur with negative, low-dominance words (e.g., scared, depressed), which confirms the conceptual distinctions made in psychology. We also show that women are more likely to report on negative feelings of being lonely as compared to men, and there are more teenagers among the tweeters that use the word 'lonely' than among the tweeters that use the word 'solitude'.

PoKi: A Large Dataset of Poems by Children

Child language studies are crucial in improving our understanding of child well-being; especially in determining the factors that impact happiness, the sources of anxiety, techniques of emotion regulation, and the mechanisms to cope with stress. However, much of this research is stymied by the lack of availability of large child-written texts. We present a new corpus of child-written text, PoKi, which includes about 62 thousand poems written by children from grades 1 to 12. PoKi is especially useful in studying child language because it comes with information about the age of the child authors (their grade). We analyze the words in PoKi along several emotion dimensions (valence, arousal, dominance) and discrete emotions (anger, fear, sadness, joy). We use non-parametric regressions to model developmental differences from early childhood to late-adolescence. Results show decreases in valence that are especially pronounced during mid-adolescence, while arousal and dominance peaked during adolescence. Gender differences in the developmental trajectory of emotions are also observed. Our results support and extend the current state of emotion development research.