We describe the data collection protocol, the possible areas of use, and the annotations for the emotional content of the recordings. This paper presents a new dataset, Multimodal Stressed Emotion (MuSE), to study the multimodal interplay between the presence of stress and expressions of affect. Although emotion classification in both singular and dyadic settings is an established area, the effects of these additional factors on the production and perception of emotion is understudied. These affective states are impacted by a combination of emotion inducers, current psychological state, and various conversational factors. Mimansa Jaiswal, Cristian-Paul Bara, Yuanhang Luo, Mihai Burzo, Rada Mihalcea and Emily Mower ProvostĮndowing automated agents with the ability to provide support, entertainment and interaction with human beings requires sensing of the users' affective state. MuSE: a Multimodal Dataset of Stressed Emotion Moreover, in order to validate the effectiveness of the data set, we also propose a machine learning approach for automatically detecting emotions in tweets for both languages, English and Spanish. We report some linguistic statistics about the data set in order to observe the difference between English and Spanish speakers when they express emotions related to the same events. In addition, each tweet was also labeled as offensive or no offensive.
A total of 8,409 in Spanish and 7,303 in English were labeled.
#SODA PLAYER EXTRACTING ADDITIONAL DEPENDANCIES PLUS#
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. We collected tweets from the Twitter platform. In order to address this shortage, we present a multilingual emotion data set based on different events that took place in April 2019. In particular, the annotated gold standard resources available are not enough. While opinion mining is a well-established task with many standard data sets and well-defined methodologies, emotion mining has received less attention due to its complexity. 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.
Information Extraction, Information RetrievalĮmoEvent: A Multilingual Emotion Corpus based on different Eventsįlor Miriam Plaza del Arco, Carlo Strapparava, L.
Group of papers sent on NovemLinks to each session We hope that you discover interesting, even exciting, work that may be useful for your own research. Packages with several sessions will be disseminated every Tuesday for 10 weeks, from until the end of January 2021.Įach session displays papers’ title and authors, with corresponding abstract (for ease of reading) and url, in like manner as the Book of Abstracts we used to print and distribute at LRECs. The ELRA Board and the LREC 2020 Programme Committee now feel that those papers should be disseminated again, in a thematic-oriented way, shedding light on specific “topics/sessions”. LREC 2020 was not held in Marseille this year and only the Proceedings were published.
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