Classifying emotions in Twitter messages using a deep neural network

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dc.contributor.authorda Silva I.R.R.
dc.contributor.authorLima A.C.E.S.
dc.contributor.authorPasti R.
dc.contributor.authorde Castro L.N.
dc.date.accessioned2024-03-12T23:55:39Z
dc.date.available2024-03-12T23:55:39Z
dc.date.issued2019
dc.description.abstract© Springer Nature Switzerland AG 2019.Many people use social media nowadays to express their emotions or opinions about something. This paper proposes the use of a deep learning network architecture for emotion classification in Twitter messages, using the six emotions model of Ekman: happiness, sadness, anger, fear, disgust and surprise. We collected the tweets from a labeled dataset that contains about 2.5 million tweets and used the Word2Vec predictive model to learn the relations of each word and transform them into numbers that the deep network receives as input. Our approach achieved a 63% accuracy with all the classes and 77% accuracy on a binary classification scheme.
dc.description.firstpage283
dc.description.lastpage290
dc.description.volume801
dc.identifier.doi10.1007/978-3-319-99608-0_32
dc.identifier.issn2194-5365
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/35404
dc.relation.ispartofAdvances in Intelligent Systems and Computing
dc.rightsAcesso Restrito
dc.subject.otherlanguageDeep learning
dc.subject.otherlanguageEmotion classification
dc.subject.otherlanguageSentiment analysis
dc.titleClassifying emotions in Twitter messages using a deep neural network
dc.typeArtigo de evento
local.scopus.citations0
local.scopus.eid2-s2.0-85061731203
local.scopus.subjectBinary classification
local.scopus.subjectEmotion classification
local.scopus.subjectEmotions modeling
local.scopus.subjectLabeled dataset
local.scopus.subjectPredictive modeling
local.scopus.subjectSocial media
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85061731203&origin=inward
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