Analisis Sentimen Review Hotel Menggunakan Algoritma Naïve Bayes Classifier
DOI:
https://doi.org/10.29103/techsi.v13i2.5596Abstract
Abstrak- Wisatawan saat melakukan pemesanan hotel seringkali mengalami kesulitan dalam menentukan hotel mana yang akan dipilih. Traveloka merupakan salah satu situs pemesanan hotel yang memiliki berbagai fitur bagi pengunjung dalam menentukan hotel yang akan dipilih. Salah satu fitur tersebut adalah ulasan yang menampilkan berbagai komentar pengunjung tentang suatu hotel. Akan tetapi semakin banyak komentar tentang suatu hotel maka pengunjung membutuhkan waktu yang lama untuk membaca dan memilih hotel yang diinginkan. Berdasarkan permasalahan tersebut, maka dibutuhkan analisis sentimen yang dapat mengolah sejumlah komentar untuk memperoleh informasi yang bermanfaat bagi pengunjung. Sistem analisis sentimen yang dibangun memiliki tujuan membuat model sentimen untuk menentukan ulasan komentar suatu hotel. Proses analisis sentimen dilakukan dengan menggunakan algoritma Pengklasifikasi Naive Bayes . Hasil pengujian bahwa klasifikasi sentimen menggunakan Naïve Bayes Classifier memperoleh hasil akurasi sebesar 90,61%, presisi sebesar 93,03%, recall sebesar 89,52% dan f-measure sebesar 90,99%.References
Liu. B, Sentiment Analysis and Subjectivity, in Handbook of Natural Language Processing, 2010.
Pang. B, dan Lee. L, Opinion Mining and Sentiment Analysis, Foundations and Trends in Information Retrieval, vol. Volume 2, no. Issue 1-2, pp. 1-135, 2008.
Pang. B, Lee. L, dan Vaithyanathan. S, Thumbs up? Sentiment Classification using Machine Learning, in Proceedings of the ACL-02 conference on Empirical methods in natural language processing, vol.Volume 10, pp. 79-86,Morristown, NJ, USA, 2002.
Elly. I, dan Mochamad. W, Penerapan Character N-Gram untuk Sentiment Analysis Review Hotel Menggunakan Algoritma Naive Bayes, Konferensi Nasional Ilmu dan Teknologi (KNIT), 8 Agustus 2015, Bekasi, 2015.
Lina. L. D, dan Girish. K. P, Analyzing Sentiment of Movie Review Data using Naive Bayes Neural Classifier, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), vol (3) Issue 4. ISSN 2278-6856, 2014.
Devi. D. P, dan Joan. S, Multinomial Naïve Bayes Classifier untuk Menentukan Review Positif atau Negatif pelanggan Website Penjualan, Seminar Nasional Inovasi dalam Desain dan Teknologi 2015.
Jingnian. C, Houkuan. H, Shengfeng. T, dan Youli. Q, Feature selection for text classification with Naïve Bayes. Expert Systems with Applications, 36(3), 5432-5435, 2009.
Qaing. Y, Ziqiong. Z, dan Rob. Law, Expert Systems with Applications Sentiment classification of online reviews to travel destinations by supervised machine learning approaches, Expert Systems With Applications, 36(3), 6527-6535, 2009.
Alper. K. U, dan Serkan. G, A novel probabilistic feature selection method for text classification. Knowledge-Based Systems36, 226-235, 2012.
Edmond. K, dan Edi. W, Penambangan Opini Pada Situs Review Film Berbahasa Indonesia, Tesis, Program Magister Ilmu Komputer Universitas Gadjah Mada, Yogyakarta, 2012.
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