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Sentiment Analysis With Sarcasm Detection On Politician’s Instagram
https://doi.org/10.22146/ijccs.66375 Aisyah Muhaddisi(1*), Bambang Nurcahyo Prastowo(2), Diyah Utami Kusumaning Putri(3)
(1) Bachelor Program of Computer Science; FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(3) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
Sarcasm is one of the problem that affect the result of sentiment analysis. According to Maynard and Greenwood (2014), performance of sentiment analysis can be improved when sarcasm also identified. Some research used Naïve Bayes and Random Forest method on sentiment analysis process. On Salles, dkk (2018) research, in some cases Random Forest outperform the performance by Support Vector Machine that known as a superior method. In this research, we did sentiment analysis on comment section on Instagram account of Indonesian politician. This research compare the accuracy of sentiment analysis with sarcasm detection and analysis sentiment without sarcasm detection, sentiment analysis with Naïve Bayes and Random Forest method then Random Forest for sarcasm detection. This research resulted in accuracy value in sentiment analysis without sarcasm detection with Naïve Bayes 61%, with Random Forest method 72%. Accuracy on sentiment analysis with sarcasm detection using Naïve Bayes – Random Forest method is 60% and using Random Forest – Random Forest method is 71%.
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[1] B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis,” Found. Trends Inf. Retr., vol. 2, pp. 1–135, 2008, doi: 10.1561/1500000011.
[2] B. Y. Liau and P. Tan, “Gaining customer knowledge in low cost airlines through text mining,” Ind. Manag. Data Syst., vol. 114, pp. 1344–1359, 2014.
[3] D. Alita and A. Isnain, “Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier,” J. komputasi, vol. 8, 2020, doi: 10.23960/komputasi.v8i2.2615.
[4] D. Antonakaki, D. Spiliotopoulos, C. V. Samaras, P. Pratikakis, S. Ioannidis, and P. Fragopoulou, “Social media analysis during political turbulence,” PLoS One, vol. 12, no. 10, pp. 1–23, 2017, doi: 10.1371/journal.pone.0186836.
[5] D. Maynard and M. A. Greenwood, “Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis,” 2014.
[6] Y. Yunitasari, A. Musdholifah, and A. Sari, “Sarcasm Detection For Sentiment Analysis in Indonesian Tweets,” Indones. J. Comput. Cybern. Syst., vol. 13, pp. 53–62, 2019.
[7] M. Bouazizi and T. Ohtsuki, “A Pattern-Based Approach for Sarcasm Detection on Twitter,” IEEE Access, vol. 4, p. 1, 2016, doi: 10.1109/ACCESS.2016.2594194.
[8] N. Couldry, S. Livingstone, and T. Markham, “Media Consumption and Public Engagement: Beyond the Presumption of Attention,” 2007, doi: 10.1057/9780230800823.
[9] I. López, S. de Maya, and L. Warlop, “When Sharing Consumption Emotions With Strangers Is More Satisfying Than Sharing Them With Friends,” J. Serv. Res., vol. 17, 2014, doi: 10.1177/1094670514538835.
[10] J. Han, M. Kamber, and J. Pei, “9 - Classification: Advanced Methods,” in Data Mining (Third Edition), Third Edition., J. Han, M. Kamber, and J. Pei, Eds. Boston: Morgan Kaufmann, 2012, pp. 393–442.
DOI: https://doi.org/10.22146/ijccs.66375
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