Abstract
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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Acknowledgements
The authors thank CAPES, CNPq, Fapesp, and MackPesquisa for the financial support. The authors also acknowledge the support of Intel for the Natural Computing and Machine Learning Laboratory as an Intel Center of Excellence in Machine Learning.
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da Silva, I.R.R., Lima, A.C.E.S., Pasti, R., de Castro, L.N. (2019). Classifying Emotions in Twitter Messages Using a Deep Neural Network. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_32
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DOI: https://doi.org/10.1007/978-3-319-99608-0_32
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