Pengaruh Penggunaan Emoji Pada Tingkat Akurasi Sentimen Di Twitter Menggunakan Metode Support Vector Machine - Dharmawan, Tio; Kinanti, Virli Galuh; Maududie, Achmad (Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK), 2025)
Opinions and preferences expressed on social media and microblogging services are very important for sentiment analysis. A Support Vector Machine (SVM) is a learning system that uses a hypothetical space in the form of a linear function in a high dimensional feature space and applies a learning bias derived from statistical learning theory. The accuracy results obtained by the Support Vector Machine method from the first topic, namely booster vaccines as a homecoming requirement, were 65% for text only and 69% for text containing emoji. The accuracy results for the second discussion topic, namely demonstrations against Jokowi for 3 periods, were 79% for text only and 82% for text containing emoji. As for the third topic regarding the scarcity of cooking oil and rising fuel prices, the accuracy obtained is 74% for text only and 76% for text containing emojis.