Klasifikasi Sentimen Ulasan Film Menggunakan Support Vector Machine, Information Gain, Dan N-grams
DOI:
https://doi.org/10.34818/eoe.v9i3.17998Abstrak
Abstrak Saat ini, orang menggunakan Internet untuk menulis opini mereka di blog, jejaring sosial, dan situs web. Situs review seperti IMDb adalah salah satu situs yang sering dikunjungi oleh pengguna Internet yang menyediakan informasi yang lengkap tentang aktor, kru, peringkat dan ulasan film yang diberikan oleh orang lain. ulasan dan peringkat film tersebut dapat mempengaruhi perilaku pembelian mereka. Pendekatan machine learning digunakan untuk memecahkan permasalahan ambiguitas opini dengan mengklasifikasikan ulasan film tersebut ke dalam sebuah sentimen. Pada penelitian ini, 50.000 dataset dari IMDb akan digunakan untuk melakukan pengujian klasifikasi Support Vector Machine dengan seleksi fitur Information Gain untuk membantu performansi dari klasifikasi SVM. Dari hasil pengujian, didapat nilai akurasi tertinggi sebesar 86% untuk unigram dan 76,4% untuk bigram pada klasifikasi SVM dengan menggunakan Information Gain sebagai seleksi fiturnya. Kata kunci : SVM, analisis sentimen, Information Gain, ulasan ilm, n-gramsReferensi
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