SINAI at SemEval-2018 Task 1: Emotion Recognition in Tweets

Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018), pages 128–132 New Orleans, Louisiana, June 5–6, 2018. ©2018 Association for Computational Linguistics SINAI at SemEval-2018 Task 1: Emotion Recognition in Tweets Flor Miriam Plaza-del-Arco, Salud Mar´ıa Jim´enez-Zafra, M. Teresa Mart´ın-Valdivia, L. Alfonso Ure˜na-L´opez Department of Computer Science, Advanced Studies Center in ICT (CEATIC) Universidad de Ja´en, Campus Las Lagunillas, 23071, Ja´en, Spain {fmplaza, sjzafra, maite, laurena}@ujaen.es Abstract Emotion classification is a new task that com- bines several disciplines including Artificial Intelligence and Psychology, although Natural Language Processing is perhaps the most cha- llenging area. In this paper, we describe our participation in SemEval-2018 Task1: Affect in Tweets. In particular, we have participated in EI-oc, EI-reg and E-c subtasks for English and Spanish languages. 1. Introduction Emotions are playing a significant role in the ef- fective communication of people. In fact, some- times, emotional intelligence is more important than cognitive intelligence for successful interac- tion (Pantic et al., 2005). Therefore, affective com- puting is a key element to the advancement of Arti- ficial Intelligence. The basic task of affective com- puting is emotion recognition. This task consists of identifying a set of emotions in a document. The identification of emotions in texts has multi- ple benefits in different areas, such as psychology to detect some psychological disorder like depres- sion (Cherry et al., 2012), e-learning to improve student motivation (Suero Montero and Suhonen, 2014) or business intelligence to know the prefe- rences of consumers (Cambria, 2016). Currently, more and more people express their emotions on social media, such as Twitter or Fa- cebook. Therefore, the role of emotion in social media is becoming more important for the resear- chers in affective computing. In this paper, we present the different systems we developed as part of our participation in SemEval- 2018 Task 1: Affect in Tweets (Mohammad et al., 2018). We have participated in EI-oc, EI-reg and E-c subtasks for English and Spanish. Below, we briefly describe these subtasks: EI-oc is an emotion intensity ordinal classi- fication task. Given a tweet and an emotion E, it consists of classifying the tweet into one of four ordinal classes of intensity of E that best represents the mental state of the twee- ter. Separate datasets are provided for anger, fear, joy, and sadness emotions. EI-reg is an emotion intensity regression task. Given a tweet and an emotion E, it con- sists of determining the intensity of E that best represents the mental state of the tweeter. The intensity of E is a real-valued score bet- ween 0 (least emotion) and 1 (most emotion). Separate datasets are provided for anger, fear, joy, and sadness emotions. E-c is an emotion multi-classification task. Given a tweet, it consists of classifying it as ’neutral’ or ’no emotion’ or as one, or more, of eleven given emotions (anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, trust) that best represent the mental state of the tweeter. The rest of the paper is organized as follows. In Section …

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