Electronic News Sentiment Analysis Application to New Normal Policy During The Covid-19 Pandemic Using Fasttext And Machine Learning

Abstract

The new phase in handling COVID-19 in Indonesia, called New Normal, gives various public perspectives regarding this policy. This study aims to analyze public sentiment towards the New Normal policy through an electronic news comment column. This study uses text data in the form of comments were collected from electronic news media sites, namely www.detik.com and www.kompas.com, and taken from the comments column on Instagram social media, namely the @detikcom account. Also, use FastText method to extract features by converting data into vector values and using three classification methods, Naive Bayes (NB), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). This study conducted a hyperparameter test to obtain the most optimal model. Testing the hyperparameters from FastText produces an optimal model with dimensions of 250, window size 8, epoch 1.000, and a learning rate of 0,0025. Hyperparameter testing was also carried out on the SVM and MLP classifiers. Hyperparameter testing of the SVM and MLP classifiers produces the most optimal model with the SVM method using the RBF kernel, C of 1.000, gamma of 10. In contrast, the MLP method uses the relu activation function, hidden size layer (250,250), adam optimizer, alpha 0,0001, and adaptive learning rate. The classification model was evaluated using K-fold cross-validation to produce an average f1score. The result is for the NB method 72,25% f1score, for the SVM method 92,21% f1score, and for the MLP method 90,75% f1score.