Sentiment Analysis of Non-Fungible Token (NFT) on Twitter Social Media Using Support Vector Machine Method - Suci Br Kembaren; Siti Saidah; Arifin Nur Rahmad; Luky Fabrianto; Novianti Madhona Faizah (Jurnal Mantik, 2022)
Social media Twitter is used as an expression of opinion for the global community, especially in Indonesia. Non-Fungible Token (NFT) also has public opinion, sentiment from opinion can be classified into two classes, negative sentiment and positive sentiment. Using the keyword "NFT Indonesia" in API from Twitter, search for tweets data obtained 2204 tweets, through the manual labeling and pre-processing stages, 1462 tweets were obtained. After tweets/data has through cleansing stage, data are separated into training data and test data and then Support Vector Machine method is used to form a model that can classify positive or negative sentiment. The results of the sentiment analysis are visualized using a pie chart. The results obtained from opinion of Indonesian netizen regarding that Non-Fungible Token (NFT) have a positive trend with a percentage of 95.90% and for negative sentiment is 4.10% with an accuracy of success in this sentiment analysis is 86%.