Relacionando Modelagem de Tópicos e Classificação de Sentimentos para Análise de Mensagens do Twitter Durante a Pandemia da COVID-19
Resumo
In 2020, COVID-19 pandemic is one of the most talked-about subjects on social networks. This subject has generated discussions of great importance about politics, economics, medical advances, people’s awareness, preventive techniques, etc. Using sentiment analysis and topic modeling techniques, in this paper, we aim to present an analysis of the messages from the social network Twitter during the pandemic of COVID-19. For this, we use a tweets dataset to train a sentiment classifier and then use the NMF algorithm to perform the interest topic generation.
Referências
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Bing Xiang and Liang Zhou. 2014. Improving twitter sentiment analysis with topic-based mixture modeling and semi-supervised training. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume2: Short Papers). 434–439.
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993–1022.
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni StJohn, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, et al.2018. Universal sentence encoder. arXiv preprint arXiv:1803.11175 (2018).
Simon S Haykin et al. 2009. Neural networks and learning machines/SimonHaykin.
Thomas Hofmann. 2013. Probabilistic latent semantic analysis. arXiv preprintarXiv:1301.6705 (2013), 289–296.
Byeongki Jeong, Janghyeok Yoon, and Jae-Min Lee. 2019. Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. International Journal of Information Management48 (2019), 280–290.
Chhinder Kaur and Anand Sharma. 2020. Twitter Sentiment Analysis on Coronavirus using Textblob. EasyChair Preprint no. 2974.
Daniel D Lee and H Sebastian Seung. 2001. Algorithms for non-negative matrixfactorization. In Advances in neural information processing systems. 556–562.
Hussin A Rothan and Siddappa N Byrareddy. 2020. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. Journal of autoimmunity (2020), 102433.
Joni Salminen, Hind Almerekhi, Milica Milenkovic, Soon-gyo Jung, Jisun An, Haewoon Kwak, and Bernard J Jansen. 2018. Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for Identifying and Classifying Hatein Online News Media.. In ICWSM. 330–339.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998–6008.
Bing Xiang and Liang Zhou. 2014. Improving twitter sentiment analysis with topic-based mixture modeling and semi-supervised training. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume2: Short Papers). 434–439.
Publicado
30/11/2020
Como Citar
PINTO, Matheus Adler Soares; JACOB JUNIOR, Antonio Fernando Lavareda ; BUSSON, Antonio José G.; COLCHER, Sérgio.
Relacionando Modelagem de Tópicos e Classificação de Sentimentos para Análise de Mensagens do Twitter Durante a Pandemia da COVID-19. In: WORKSHOP DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 26. , 2020, São Luís.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 61-64.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2020.13064.
