Mineração de opinião em mídias sociais com aprendizado de máquina

• Autor(a) principal: Brandão, Jhonathan de Godoi
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFG
• dARK ID: ark:/38995/0013000005qvm
• Texto Completo: http://repositorio.bc.ufg.br/tede/handle/tede/11185
• topic: Análise de sentimentos; Mineração de opinião; Aprendizado de máquina; Sentiment analysis; Opinion mining; Machine learning; ENGENHARIAS

The aim of this work is to develop a tool for that optimizes supervised machine learning models in order to classify polarity of opinions in tweets. Five different datasets are used, which are prepared, preprocessed and then used as input for the training and evaluation stage of machine learning models. The best accuracy results obtained in the training and evaluation of the models are 82.45% for the data without preprocessing × 78.83% with all the proposed preprocessing for the dataset using the Naive Bayes classifier. Finally, hyperparametric optimization of the classifiers and selection of the model that obtains the best accuracy is performed. The optimized model achieves an accuracy greater than 90% for some data sets. The supervised learning techniques depend on labeled data for training, the proposed method produces similar performances for datasets of varying sizes, which allows the development of optimized classification models with reduced amount of labeled data.