SVM UNTUK SENTIMENT ANALYSIS CALON KEPALA DAERAH BERDASAR DATA KOMENTAR VIDEO DEBAT PILKADA DI YOUTUBE
Abstract
YouTube is a social media that is widely used by people to share videos that contain various types of content. Unregistered users can view videos, while registered users can upload videos and provide an unlimited number of comments. Mostly, videos on YouTube are music clips (video clips), movie trailers, educational videos, review videos, discussion videos, and debate or dialogue. Users’ comments and opinions on YouTube can be used as an indicator to see their inclination to a particular regional head candidate; therefore, comments can be a source of data on public opinion and sentiment in a social study.
Inside the candidate team for regional head elections, sentiment analysis is used as a rationale for determining policies and campaign tactics to increase the popularity of their candidate and to test whether the candidate is well accepted in the public eye.
Support Vector Machine (SVM) is a sentiment analysis model. SVM belongs to the algorithm group with the supervised technique. The three groups of the categorization used in SVM will look for the maximum value of the hyperplane which divides the test room into separate classes. SVM is a computational algorithm that requires a large operation because it includes discretization, normalization, and repeated product point operations.
It is expected that Support Vector Machine (SVM) can automatically process comment data on the debate video of regional head candidates posted on YouTube, and then continually classify sentiment analysis of people’s comments on the regional head candidates. Additionally, this study is significant to become a further reference for those interested in developing SVM.
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References
R. Feldman, “Techniques and applications for sentiment analysis,” Commun. ACM, vol. 56, no. 4, pp. 82–89, Apr. 2013.
E. Haddi, X. Liu, and Y. Shi, “The role of text pre-processing in sentiment analysis,” Procedia Computer Science, vol. 17, no. 0, pp. 26 – 32, 2013, first International Conference on Information Technology and Quantitative Management.
H. Tang, S. Tan, and X. Cheng, “A survey on sentiment detection of reviews,” Expert Systems with Applications, vol. 36, no. 7, pp. 10760 – 10773, 2009.
M. Thelwall, K. Buckley, and G. Paltoglou, “Sentiment in twitter events,” J. Am. Soc. Inf. Sci. Technol., vol. 62, no. 2, pp. 406–418, Feb. 2011.
D V Nagarjuna Devi, Chinta Kishore Kumar, Siriki Prasad, “A Feature Based Approach for Sentiment Analysis by Using Support Vector Machine”, 2016.
Upma Kumari, Arvind K Sharma Dr., Dinesh Soni, “Sentiment Analysis of Smart Phone Product Review using SVM Classiification Technique”, 2017.
Nurulhuda Zainuddin, Ali Selamat, “Sentiment Analysis Using Support Vector Machine”, 2014.
Rofiqoh, U., Perdana, R. S., dan Fauzi, M. A. 2017. Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter Dengan Metode Support Vector Machine dan Lexicon Based Features. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol. 1, No. 12, Hal: 1725-1732.
Wahid, D. H. dan Azhari. 2016. Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity. Indonesian Journal of Computing and Cybernetics Systems (IJCCS), Vol. 10 , No. 2, Hal: 207-218.
Suyanto. 2019. Data Mining: untuk Klasifikasi dan Klasterisasi Data Edisi Revisi. Bandung: Informatika.
Lu,Shuxia., Jin, Zhao., 2017, Improved Stochastic Gradient Descent Algorithm for SVM, International Journal of Recent Engineering Science(IJRES). Vol 4, Issue 4, Hal. 28 – 31.
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