Comparison of SVM & Naãve Bayes Algorithm for Sentiment Analysis Toward West Java Governor Candidate Period 2018-2023 Based on Public Opinion on Twitter

Comparison of SVM & Naãve Bayes Algorithm for Sentiment Analysis Toward West Java Governor Candidate Period 2018-2023 Based on Public Opinion on Twitter

• By Kristiyanti, Dinar Ajeng; Umam, Akhmad Hairul; Wahyudi, Mochamad; Amin, Ruhul; Marlind, Linda;
• Jurnal Internasional Bereputasi
• 146

Opinion is a statement conveyed by a person or
group of people in addressing a problem by providing predictions
or expectations about the event. No guarantee that an opinion
automatically will be true because it is not reinforced by the facts,
it is subjective, and there is a different opinion about an event.
Everyone has different views and same rights to express opinions
or give opinions toward particular event. Public opinion is view
of someone for certain problem comes out due to prior
conversation with another person who may have an effect on the
opinion given. Public opinion comes from a discussion process in
addressing the problem then lead to a conclusion as a joint
decision and a shared opinion. One of the media to convey public
opinion is through social media like twitter. Public opinion about
the election of West Java governor candidate period 2018-2023
on twitter was increasingly widespread. There are several
sentiments emerged for four candidates elected on twitter
accounts such as Ridwan Kamil-Uu Ruzhanul Ulum, Tubagus
Hasanuddin-Anton Charliyan, Sudrajat-Ahmad Syaikhu, and
Deddy Mizwar-Dedi Mulyadi. Therefore, it is necessary to
classify the sentiments to the existing opinion so that it can be
predicted in advance which of the governor candidate pair of
West Java who has more positive and predictable sentiments will
be elected as governor period 2018-2023. The data used by the
researchers is tweet in Indonesian Language with keywords
Rindu, Hasanah, Asyik, 2DM with datasets number is 800 tweets.
There are many classification techniques commonly used for
opinion sentiment analysis. This study compares two
classification techniques namely Support Vector Machine
Algorithm (SVM) and Naïve Bayes Classifier (NBC). The results
show that the Algorithm of Naïve Bayes Classifier (NBC) has a
higher accuracy level of Support Vector Machine (SVM), up to
94% for Deddy Mizwar-Dedi Mulyadi.

• Katakunci West Java Governor Candidate; Opinion Public; Sentiment Analysis; Twitter.
• URL https://ieeexplore.ieee.org/document/8674352
• Penerbit ,

REFERENSI

N. Nurulliah, “Empat Pasangan Calon Pilgub Jabar 2018 Resmi
Ditetapkan,” http://www.pikiran-rakyat.com, Bandung, Feb-2018.
[2] P. J. B. KPU, “PENETAPAN PASANGAN CALON GUBERNUR
DAN WAKIL GUBERNUR JAWA BARAT TAHUN 2018,” 2018,
p. 1.
[3] A. M. Kaplan and M. Haenlein, “Users of the world, unite! The
Accuracy: 75.50.00%, +/- 6.24% (Micro: 89.00%)
True
Positive
True
Negative
Class
Precission
Predictions Positive 62 11 84.93%
Predictions Negative 38 89 70.08%
Class Recall 62.00% 89.00%
Support Vector Machine (SVM)
West Java Governor Candidate
Period 2018-2023 Accuracy (%) TP Rate
RINDU 74.50 85
HASANAH 63.50 30
ASYIK 72.00 96
2DM 75.50 62
Naïve Bayes
West Java Governor Candidate
Period 2018-2023 Accuracy (%) TP Rate
RINDU 89.00 79
HASANAH 84.50 92
ASYIK 87.00 90
2DM 94.00 90
| The 6th International Conference on Cyber and IT Service Management (CITSM 2018)
Inna Parapat Hotel – Medan, August 7-9, 2018
challenges and opportunities of Social Media,” Bus. Horiz., vol. 53,
no. 1, pp. 59–68, 2010.
[4] G. A. Buntoro, “Analisis Sentimen Calon Gubernur DKI Jakarta
2017 Di Twitter,” Integer J. Maret, vol. 1, no. 1, pp. 32–41, 2016.
[5] M. R. Yarrow, J. A. Clausen, and P. R. Robbins, “The Social
Meaning of Mental Illness,” J. Soc. Issues, vol. 11, no. 4, pp. 33–48,
Oct. 2010.
[6] M. Wahyudi and D. A. Kristiyanti, “Sentiment Analysis of
Smartphone Product Review Using Support Vector Machine
Algorithm-Based Particle Swarm Optimization.,” J. Theor. Appl.
Inf. Technol., vol. 91, no. 1, p. 189, 2016.
[7] J. Spencer and G. Uchyigit, “Sentimentor: Sentiment analysis of
twitter data,” CEUR Workshop Proc., vol. 917, pp. 56–66, 2012.
[8] R. Dehkharghani, H. Mercan, A. Javeed, and Y. Saygin,
“Sentimental causal rule discovery from Twitter,” Expert Syst.
Appl., vol. 41, no. 10, pp. 4950–4958, Aug. 2014.
[9] M. Kaya, G. Fidan, and I. H. Toroslu, “Sentiment analysis of
Turkish Political News,” in Proceedings - 2012 IEEE/WIC/ACM
International Conference on Web Intelligence, WI 2012, 2012, pp.
174–180.
[10] I. Habernal, “Sentiment Analysis in Czech Social Media Using
Supervised Machine Learning,” no. June, pp. 65–74, 2013.
[11] S. Ceri, “Predicting Political Elections with Social Networks (The
Case of Twitter in the 2012 U.S. Presidential Election),” Politecnico
Di Milano, 2014.
[12] G. A. Buntoro, T. B. Adji, and A. E. Purnamasari, “Sentiment
Analysis Candidates of Indonesian Presiden 2014 with Five Class
Attribute,” Int. J. Comput. Appl., vol. 136, no. 2, pp. 23–29, 2016.
[13] S. Hidayat, “An Islamic Party in Urban Local Politics: The PKS
Candidacy at the 2012 Jakarta Gubernatorial Election,” J. Polit.,
vol. 2, pp. 5–40, 2016.
[14] M. Iqbal, “Ridwan Kamil for Mayor A study of a Political Figure on
Twitter,” Stockholm University, 2016.
[15] J. S. LEE, “Citizens’ Political Information Behaviors During
Elections On Twitter In South Korea: Information Worlds Of
Opinion Leaders,” The Florida State University, 2016.
[16] K. Charalampidou, “Estimating Popularity by Sentiment and
Polarization Classification on Social Media,” Delft University of
Technology, 2012.
[17] K. Miichi, “The Role of Religion and Ethnicity in Jakarta’s 2012
Gubernatorial Election.,” J. Curr. Southeast Asian Aff., vol. 33, no.
1, pp. 55–83, 2014.
[18] A. S. H. Basari, B. Hussin, I. G. P. Ananta, and J. Zeniarja,
“Opinion Mining of Movie Review using Hybrid Method of
Support Vector Machine and Particle Swarm Optimization,”
Procedia Eng., vol. 53, pp. 453–462, Jan. 2013.
[19] D. A. Kristiyanti and M. Wahyudi, “Feature Selection Based on
Genetic Algorithm , Particle Swarm Optimization and Principal
Component Analysis for Opinion Mining Cosmetic Product
Review,” in 2017 5th International Conference on Cyber and IT
Service Management (CITSM), 2017.
[20] Q. Ye, Z. Zhang, and R. Law, “Sentiment classification of online
reviews to travel destinations by supervised machine learning
approaches,” Expert Syst. Appl., vol. 36, no. 3, pp. 6527–6535, Apr.
2009.
[21] A. S. Adinugroho, “Implementasi Analisis Sentimen Menggunakan
Algoritma Naive Bayes Terhadap Pemilihan Gubernur DKI Jakarta
Pada Media Sosial Twitter,” UDINUS, 2016.
[22] J. H. Yang, “Indonesian Presidential Election: Will Social Media
Forecasts Prove Right?,” RSIS Comment. , no. 120, pp. 1–3, 2014.
[23] N. W. S. Saraswati, “Naïve Bayes Classifier Dan Support Vector
Machines Untuk Sentiment Analysis,” Semin. Nas. Sist. Inf.
Indones., pp. 587–591, 2013.
[24] A. F. Hadi, D. B. C. W, M. Hasan, and A. D. Penelitian, “Text
Mining Pada Media Sosial Twitter Studi Kasus: Masa Tenang
Pilkada Dki 2017 Putaran 2,” 2017.
[25] M. H. Rasyadi, “Analisis Sentimen Pada Twitter Menggunakan
Metode Naïve Bayes (Studi Kasus Pemilihan Gubernur Dki Jakarta
2017),” Institut Pertanian Bogor, 2017.
[26] A. R. T. Lestari, R. S. Perdana, and M. A. Fauzi, “Analisis Sentimen
Tentang Opini Pilkada Dki 2017 Pada Dokumen Twitter Berbahasa
Indonesia Menggunakan Näive Bayes dan Pembobotan Emoji,” J.
Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 12, pp. 1718–
1724, 2017.
[27] Z. Zhang, Q. Ye, Z. Zhang, and Y. Li, “Sentiment classification of
Internet restaurant reviews written in Cantonese,” Expert Syst.
Appl., vol. 38, no. 6, pp. 7674–7682, Jun. 2011.
[28] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede,
“Lexicon-Based Methods for Sentiment Analysis,” Comput.
Linguist., vol. 37, no. 2, pp. 267–307, 2011.
[29] A. Sarlan, C. Nadam, and S. Basri, “Twitter sentiment analysis,”
Conf. Proc. - 6th Int. Conf. Inf. Technol. Multimed. UNITEN Cultiv.
Creat. Enabling Technol. Through Internet Things, ICIMU 2014,
no. November 2014, pp. 212–216, 2015.
[30] M. Annett and G. Kondrak, “A comparison of sentiment analysis
techniques: Polarizing movie blogs,” Lect. Notes Comput. Sci.
(including Subser. Lect. Notes Artif. Intell. Lect. Notes
Bioinformatics), vol. 5032 LNAI, no. Figure 1, pp. 25–35, 2008.
[31] K. Khan, B. Baharudin, A. Khan, and A. Ullah, “Mining opinion
components from unstructured reviews: A review,” J. King Saud
Univ. - Comput. Inf. Sci., May 2014.
[32] T. Carpenter and T. Way, “Tracking Sentiment Analysis through
Twitter,” Proc. 2012 Int. Conf. Inf. Knowl. Eng. , no. Figure 1, 2013.
[33] A. Pak and P. Paroubek, “Twitter as a Corpus for Sentiment
Analysis and Opinion Mining,” Ijarcce, vol. 5, no. 12, pp. 320–322,
2016.
[34] P. Gonçalves, M. Araújo, F. Benevenuto, and M. Cha, “Comparing
and Combining Sentiment Analysis Methods,” in Proceedings of the
first ACM conference on Online social networks, 2014, pp. 27–38.
[35] B. Pang and L. Lee, Opinion Mining and Sentiment Analysis, vol. 2,
no. 1–2. Boston, USA: Foundations and Trends R? in Information
Retrieval, 2008.
[36] R. Prabowo and M. Thelwall, “Sentiment Analysis: A Combined
Approach,” J. Informetr., vol. 3, no. 2, pp. 143–157, 2009.
[37] A. S. H. Basari, B. Hussin, I. G. P. Ananta, and J. Zeniarja,
“Opinion mining of movie review using hybrid method of support
vector machine and particle swarm optimization,” Procedia Eng.,
vol. 53, pp. 453–462, 2013.
[38] J.-S. Chou, M.-Y. Cheng, Y.-W. Wu, and A.-D. Pham, “Optimizing
parameters of support vector machine using fast messy genetic
algorithm for dispute classification,” Expert Syst. Appl., vol. 41, no.
8, pp. 3955–3964, Jun. 2014.
[39] S. Sharma, “Damage Detection Method Using Support Vector
Machine and First Three Natural Frequencies for Shear Structures,”
WORCESTER POLYTECHNIC INSTITUTE In, 2008.
[40] M. Zhao, C. Fu, L. Ji, K. Tang, and M. Zhou, “Feature selection and
parameter optimization for support vector machines: A new
approach based on genetic algorithm with feature chromosomes,”
Expert Syst. Appl., vol. 38, no. 5, pp. 5197–5204, May 2011.
[41] J. Chen, H. Huang, S. Tian, and Y. Qu, “Feature selection for text
classification with Naïve Bayes,” Expert Syst. Appl., vol. 36, no. 3
PART 1, pp. 5432–5435, 2009.
[42] A. K. Uysal and S. Gunal, “A novel probabilistic feature selection
method for text classification,” Knowledge-Based Syst., vol. 36, pp.
226–235, 2012.
[43] V. I. Santoso, G. Virginia, Y. Lukito, U. Kristen, and D. Wacana,
“Penerapan Sentiment Analysis Pada Hasil Evaluasi Dosen Dengan
Metode Support Vector Machine,” vol. 14, no. 1, pp. 79–83, 2017.
[44] J. Han, J. Pei, and M. Kamber, Data mining: concepts and
techniques, vol. 278. Elsevier, 2011.
[45] I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining:
Practical machine learning tools and techniques. Belanda: Morgan
Kaufmann, 2016.