EmoEvent: A Multilingual Emotion Corpus based on different Events

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 1492–1498 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 1492 EmoEvent: A Multilingual Emotion Corpus based on different Events Flor Miriam Plaza-del-Arco1, Carlo Strapparava2, L. Alfonso Ure˜na-L´opez1, M. Teresa Mart´ın-Valdivia1 1Department of Computer Science, Advanced Studies Center in ICT (CEATIC), Universidad de Ja´en, Spain {fmplaza, laurena, maite}@ujaen.es 2Fondazione Bruno Kessler, Trento, Italy strappa@fbk.eu Abstract In recent years emotion detection in text has become more popular due to its potential applications in fields such as psychology, marketing, political science, and artificial intelligence, among others. While opinion mining is a well-established task with many standard datasets and well-defined methodologies, emotion mining has received less attention due to its complexity. In particular, the annotated gold standard resources available are not enough. In order to address this shortage, we present a multilingual emotion dataset based on different events that took place in April 2019. We collected tweets from the Twitter platform. Then one of seven emotions, six Ekman’s basic emotions plus the “neutral or other emotions”, was labeled on each tweet by 3 Amazon MTurkers. A total of 8,409 in Spanish and 7,303 in English were labeled. In addition, each tweet was also labeled as offensive or non-offensive. We report some linguistic statistics about the dataset in order to observe the difference between English and Spanish speakers when they express emotions related to the same events. Moreover, in order to validate the effectiveness of the dataset, we also propose a machine learning approach for automatically detecting emotions in tweets for both languages, English and Spanish. Keywords: emotion mining, emotion annotation, social media, affective-computing 1. Introduction Emotions can be defined as states that reflect evaluative judgments (appraisal) of the environment, the self and other social agents, in light of the organisms goals and beliefs, which motivate and coordinate adaptive behavior (Hudlicka, 2011). In psychology, emotions are categorized into basic emotions (those considered universal and innate in human beings such as joy, anger or fear), and complex emotions (those perceived as the result of the combination of the basic ones and that are hard to classify under a single term such as guilt, pride or shame). In recent decades, research on emotion has become popular in numerous fields including psychology, sociology, neu- roscience, endocrinology, medicine, history, and computer science. Emotion analysis in computational linguistics con- sists of identifying discrete emotion expressed in text and is seen as a natural evolution of sentiment analysis and its more fine-grained model (Seyeditabari et al., 2018). How- ever, as it is a more difficult task than sentiment analysis, this field still has a long way to go. The automatic detection of emotions in texts is becoming increasingly important due to its vast potential applications in a number of areas, such as marketing to modify or im- prove business strategies according to the emotion of cus- tomers, psychology to detect personal traits, political sci- ence to track public emotion on any national, international …

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