Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter Dengan Metode Support Vector Machine dan Lexicon Based Features - Umi Rofiqoh; Rizal Setya Perdana; Mochammad Ali Fauzi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2017)
Sentiment analysis is a part of research from Text Mining which is usefull to classify text documents contained opinion based on sentiment. Text document that is used in research comes from Twitter from people's opinion about cellular telecommunication service provider. The used method is Support Vector Machine with using Lexicon Based Features as its feature renewal instead of using TF-IDF features. The used data in this research is 300 data which divided into two types of data with ratio 70% for training data and 30% for testing data. The result of system accuracy that is obtained from sentiment analysis using Support Vector Machine and Lexicon Based Features method is 79% using degree value 2, constant learning rate value 0.0001, and maximum iteration is 50 times. While sentiment analysis system without using Lexicon Based Features is resulting accuracy at 84% with the same parameter values.