Classifying emotions in Twitter messages using a deep neural network
Tipo
Artigo de evento
Data de publicação
2019
Periódico
Advances in Intelligent Systems and Computing
Citações (Scopus)
0
Autores
da Silva I.R.R.
Lima A.C.E.S.
Pasti R.
de Castro L.N.
Lima A.C.E.S.
Pasti R.
de Castro L.N.
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Resumo
© 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.
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Assuntos Scopus
Binary classification , Emotion classification , Emotions modeling , Labeled dataset , Predictive modeling , Social media