Penggunaan Algoritma Naïve Bayes dengan Polarity Textblob untuk Analisis Sentimen pada Acara ASEAN CUP 2024 U-16 di Media Sosial Twitter - Arya Erlangga; Yani Parti Astuti; Etika Kartikadarma; Sindhu Rakasiwi; Egia Rosi Subhiyakto (Switch : Jurnal Sains dan Teknologi Informasi, 2025)
Football is a popular sport in the world and is enjoyed by people of all ages. The Indonesia U-16 national team played in the ASEAN CUP 2024 event in this field. Twitter users gave their support through #timnasday during the event. This provided many forms of support for the Indonesian national team which made it difficult to identify positive, neutral, and negative sentiments. This requires the use of lexicon-based textblob to perform automatic labeling. In the labeling results using textblob from a total of 1138 user tweet data resulted in positive sentiment values of 50.9% or 579 positive data, neutral 33.7% or 384 neutral data, and negative 15.4% or 175 negative data. In the test results using one of the machine learning from the naïve bayes classifier, namely gaussian naïve bayes with the division of test data and training data of 0.3 and 0.7, the accuracy value is 98.53%