UJI COBA STEMMING POTTER PADA SKEMA SISTEM PENENTUAN PERINGKAT BUKU BERDASARKAN TESTIMONI MENGGUNAKAN KESAMAAN SEMANTIK
Keywords:
Testimoni, Opinion Mining, Analisis Sentimen
Abstract
Testimony is an opinion mining or could be part of sentiment analysis, which can defined as a process of understanding, extract and process the textual data automatically to get the sentiment of information contained in an opinion sentence. Testimonials about a product is very important in determining the purchase of a product. Sentences which is contained in the testimonial could be negative or positive view. In this paper, sentiment analysis is done to see opinions or tendency of opinions towards a book products, whether or opine tend to view negative or positive. The magnitude of the effect and benefits of sentiment analysis led to research and application based on sentiment analysis rapidly grow. Methodology which is done in the first step in this paper is preprocessing which includes tokenizing text and stemming, and continued with the process of forming the corpus database and the process of determining the ranking of results testimony. In this study, the algorithm which is used for stemming process is a potter algorithm.
References
[1] Agusta Ledy, “Perbandingan Algoritma Stemming Porter Dengan Algoritma Nazief & Adriani Untuk Stemming Dokumen Teks Bahasa Indonesia”, Konferensi Nasional Sistem dan Informatika , November 14, 2009.
[2] A. Onan, S. Korukoglu, “ Exploring Performance of Instance Selection Methods in Text Sentiment Classification”, Springer International ublishing Switzerland, Artificial Intelligence Perspectives in Intelligent System, Advanced in Intelligent System and Computering, 2016.
[3] El-din D.M, Mokhtar, H.M.O, Ismael Osama, “ Online Paper Review Analysis”, International Journalof Advanced Computer Science and Application, IJACSA, Vol. 6, No. 9, 2015.
[4] Hu Wei Hsio, Chen Liang Yen, Hsu Tze P, “ A Novel Approach to Rate and Summarize Online Reviews According to User- Specified Aspects”, Journal of Electronic Commerce Research, Vol. 17, No. 2, 2016.
[5] Liu, B, Hand Book of natural Languange Processing, Chapter Sentiment Analysis and Analysis, 2nd Edition, Chapman & Hall/CRC Press, 2010.
[6] Lyu Kigon, Kim Hyeoncheol, “ Sentiment Analysis Using Wrd Polarity of Social Media”, Springer Science , Businee Media, New York, 2016
[7] McGinty,L,Smyth,B, “Adaptive selection :analysis of critiquing and preferencebased feed back in conversation on recommender systems”, Int J Electron Commerce 11, Vol. 2,pp. 35-57, 2006.
[8] Melville, Wojciech Gryc, “ Sentiment Analysis of Blogs by Combining Lexical Knowledge with Text Classification”, ACM Paris, 2009.
[9] Rudy Prabowo, Mike Thelwall, “ Sentiment Analysis : A Combined Approach”, Journal of Informatics 3, pp. 143-157, 2009.
[10] Rui Xia, Chengqing Zong, Shoushan Li, “Ensemble of Feature Sets and Classification Algorithms for Sentiment Classification”, Informations Sciences 181, pp 1138-1152, 2011.
[11] Samodra. J, Sumpeno, S, Hariadi, M., “ Klasifikasi Dokumen Teks Bahasa Indonesia dengan Menggunakan Naïve Bayes”, Prosiding Seminar Nasional Electrical, Informatics and IT’S Education, 2009.
[12] Scafer, J.B, Konsta, J.A. and Riedl, J, “ Item-Base Collaborative Filtering Recommender Algorithm”, www10, 2016.
[13] Songbo Tan, Jin Zhang, “ An Empirical Study of Sentiment analysis for Chinese Documents”, Expert System With application 34, pp 2622-2629, 2008.
[14] Turney, Peter D, “ Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews”, Presented at the Association for Computational Linguistic 40 Anniversary Meeting, New Brunswick, N.J, 2002.
[15] Tala, F.Z, “ A study of Stemming Effect on Information Retrieval in Bahasa Indonesia”, Master Thesis, Institute for Logic, Language and Computation Universiteit Van Amsterdam The Netherland, 2003.
[16] Vinodhini, G, Chandra Sekaran, R,“ Sentiment Analysis and Opinion Mining : A Survey”, International Journal of Advanced research in Computer Science and Software Engineering, Vol. 6, No. 6, June, 2012.
[17] Ziqiong Zhang, qiang Ye, Zili Zhang, Yijun Li, “ Sentiment Classification of Internet Restaurant Reviews Written in Cantonese”, Expert System with Application, 2011.
[2] A. Onan, S. Korukoglu, “ Exploring Performance of Instance Selection Methods in Text Sentiment Classification”, Springer International ublishing Switzerland, Artificial Intelligence Perspectives in Intelligent System, Advanced in Intelligent System and Computering, 2016.
[3] El-din D.M, Mokhtar, H.M.O, Ismael Osama, “ Online Paper Review Analysis”, International Journalof Advanced Computer Science and Application, IJACSA, Vol. 6, No. 9, 2015.
[4] Hu Wei Hsio, Chen Liang Yen, Hsu Tze P, “ A Novel Approach to Rate and Summarize Online Reviews According to User- Specified Aspects”, Journal of Electronic Commerce Research, Vol. 17, No. 2, 2016.
[5] Liu, B, Hand Book of natural Languange Processing, Chapter Sentiment Analysis and Analysis, 2nd Edition, Chapman & Hall/CRC Press, 2010.
[6] Lyu Kigon, Kim Hyeoncheol, “ Sentiment Analysis Using Wrd Polarity of Social Media”, Springer Science , Businee Media, New York, 2016
[7] McGinty,L,Smyth,B, “Adaptive selection :analysis of critiquing and preferencebased feed back in conversation on recommender systems”, Int J Electron Commerce 11, Vol. 2,pp. 35-57, 2006.
[8] Melville, Wojciech Gryc, “ Sentiment Analysis of Blogs by Combining Lexical Knowledge with Text Classification”, ACM Paris, 2009.
[9] Rudy Prabowo, Mike Thelwall, “ Sentiment Analysis : A Combined Approach”, Journal of Informatics 3, pp. 143-157, 2009.
[10] Rui Xia, Chengqing Zong, Shoushan Li, “Ensemble of Feature Sets and Classification Algorithms for Sentiment Classification”, Informations Sciences 181, pp 1138-1152, 2011.
[11] Samodra. J, Sumpeno, S, Hariadi, M., “ Klasifikasi Dokumen Teks Bahasa Indonesia dengan Menggunakan Naïve Bayes”, Prosiding Seminar Nasional Electrical, Informatics and IT’S Education, 2009.
[12] Scafer, J.B, Konsta, J.A. and Riedl, J, “ Item-Base Collaborative Filtering Recommender Algorithm”, www10, 2016.
[13] Songbo Tan, Jin Zhang, “ An Empirical Study of Sentiment analysis for Chinese Documents”, Expert System With application 34, pp 2622-2629, 2008.
[14] Turney, Peter D, “ Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews”, Presented at the Association for Computational Linguistic 40 Anniversary Meeting, New Brunswick, N.J, 2002.
[15] Tala, F.Z, “ A study of Stemming Effect on Information Retrieval in Bahasa Indonesia”, Master Thesis, Institute for Logic, Language and Computation Universiteit Van Amsterdam The Netherland, 2003.
[16] Vinodhini, G, Chandra Sekaran, R,“ Sentiment Analysis and Opinion Mining : A Survey”, International Journal of Advanced research in Computer Science and Software Engineering, Vol. 6, No. 6, June, 2012.
[17] Ziqiong Zhang, qiang Ye, Zili Zhang, Yijun Li, “ Sentiment Classification of Internet Restaurant Reviews Written in Cantonese”, Expert System with Application, 2011.
