Pengaruh Penggunaan Emoji Pada Tingkat Akurasi Sentimen Di Twitter Menggunakan Metode Support Vector Machine
Abstract
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.
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DOI: https://doi.org/10.31284/p.snestik.2025.7046
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