Sentiment Analysis pada Data Twitter dengan Pendekatan Naïve Bayes Multinomial

Bagus Setya Rintyarna

Abstract


Sebagai platform di mana user bertukar informasi dalam bentuk pesan pendek, link ke website lain, gambar maupun video, twitter telah berevolusi menjadi platform microblogging yang menjadi sumber informasi bagi banyak permasalahan karena karakteristiknya yang bersifat real time. Salah satu informasi yang penting diekstraksi dari data twitter adalah opini atau sentimen. Teknik untuk mengekstraksi sentimen dengan pendekatan komputasional disebut sebagai Sentiment Analysis. Penelitian ini mengusulkan eksperimen dengan teknik Sentiment Analysis pada dataset twitter dengan metode Multinomial Naïve Bayes untuk kategorisasi data teks. Tool yang dipergunakan adalah WEKA. Pada penelitian ini dievaluasi pengaruh algoritma stemming yang berbeda, yaitu : 1) IteratedLovinsStemmer, 2) LovinsStemmer, 3) NullStemmer dan 4) SnowBallStemmer. Evaluasi kinerja disajikan dalam enam matriks parameter yaitu TP Rate, FP Rate, Precision, Recall dan F Measure serta ROC.

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References


Agarwal, A., Xie, B., Vovsha, I., Rambow,O., & Passonneau, R. (2011).Sentiment Analysis of Twitter Data.In Proc. ACL 2011 Workshop onLanguages in Social Media, (pp. 30–38).

Cámara, E. M., Valdivia, M. T. M., López,L. A. U., & Ráez, A. R. M. (2012). Sentiment analysis in Twitter Sentiment analysis in Twitter. Natural Language Engineering. http://doi.org/10.1017/S1351324912000332

Kouloumpis, E., Wilson, T., & Moore, J.(2011). Twitter sentiment analysis: The good the bad and the omg! Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM 11), 538–541.Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/download/2857/3251?iframe=true&width=90%25&height=90%25

Kumar, A., & Sebastian, T. M. (2012).Sentiment Analysis on Twitter.International Journal of ComputerScience Issues,9(4), 372–378.

Medhat, W., Hassan, A., & Korashy, H.(2014). Sentiment analysis algorithms and applications : A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. http://doi.org/10.1016/j.asej.2014.04.011

Mullen, T., & Collier, N. (2011). Lexiconbased methods for sentiment analysis. Computational Linguistics,37(2), 267–307.

Pak, A., & Paroubek, P. (2010). Twitter as a Corpus for Sentiment Analysis and Opinion Mining. In In Proceedings of the Seventh Conference on International Language Resources and Evaluation (pp. 1320–1326). http://doi.org/10.1371/journal.pone.0026624

Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis, 2, 1–135. http://doi.org/10.1561/1500000001

Rintyarna, B. S., & Sarno, R. (2016). Adapted Weighted Graph for Word Sense Disambiguation. In 2016 4th International Conference on Information and Communication Technology (ICoICT) (Vol. 4, pp. 60–64). http://doi.org/10.1109/ICoICT.2016.75

Saif, H., He, Y., Fernandez, M., & Alani,H. (2014). Contextual semantics for

sentiment analysis of Twitter. Information Processing and

Management, 52(1), 5–19. http://doi.org/10.1016/j.ipm.2015.01.005

Suryani, A. A., Arieshanti, I., Yohanes, B.W., Subair, M., Budiwati, S. D., &

Rintyarna, B. S. (2016). Enriching English Into Sundanese and

Javanese Translation List Using Pivot Language. In 2016 International

Conference on Information, Communication Technology and System (IC (pp. 167–171).

Taboada, M., Brooke, J., & Tofiloski, M.(2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(September 2010), 267–307. Retrieved from

http://www.mitpressjournals.org/doi/abs/10.1162/COLI_a_00049




DOI: https://doi.org/10.32528/justindo.v2i1.1034

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