Abstract
Currently, the spread of information Covid-19 is spreading rapidly. Not only through electronic media, but this information is also disseminated by user posts on social media. Due to the user text posted is varies greatly, it’s needs a special approach to classify these types of posts. This research aims to classify the public sentiment towards the handling of COVID-19. The data from this study were obtained from the social media application i.e., Twitter. This study uses a derivative of the Naïve Bayes algorithm, namely Multinomial Nave Bayes to optimize the classification results. Three class labels are used to classify public sentiment namely positive, negative, and neutral sentiments. The stage starts with text preprocessing; cleaning, case folding, tokenization, filtering and stemming. Then proceed with weighting using the TF-IDF approach. To evaluate the classification results, data is tested using confusion matrix by testing accuracy, precision, and recall. From the test results, it is found that the weighted average for precision, recall and accuracy is 74%. Research shows that the accuracy of the proposed method has fair classification levels.
Highlights
The spread of information Covid-19 is spreading rapidly
Berdasarkan tabel 7, untuk menghitung recall adalah dengan pembagian antara jumlah klasifikasi yang benar diharapkan algoritma dapat memperhatikan satu kata atau bahkan per satu kalimat
Noor Hafidz, “Klasifikasi Sentimen pada Twitter “Improving the accuracy of text classification using stemming
Summary
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