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 Na?ve 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 Na?ve Bayes and Random Forest method then Random Forest for sarcasm detection. This research resulted in accuracy value in sentiment analysis without sarcasm detection with Na?ve Bayes 61%, with Random Forest method 72%. Accuracy on sentiment analysis with sarcasm detection using Na?ve Bayes – Random Forest method is 60% and using Random Forest – Random Forest method is 71%.
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机译：讽刺是影响情绪分析结果的问题之一。根据Maynard和Greenwood（2014年），当讽刺也确定时，可以提高情感分析的性能。一些研究在情绪分析过程中使用了Na ve Bayes和随机林法。在Salles，DKK（2018）研究中，在某些情况下，随机森林优于支持传染媒介机器的性能，称为卓越的方法。在这项研究中，我们对印度尼西亚政治家Instagram账户的评论部分进行了情绪分析。该研究比较了讽刺检测和分析情绪情绪分析的准确性，无讽刺检测，与Na ve + Ve Bayes和随机森林方法的情绪分析，随机森林进行讽刺检测。该研究导致情绪分析中的精度值，没有讽刺检测，具有Naαve贝雷斯61％，随机森林方法72％。使用Na've贝叶斯讽刺检测对讽刺检测的情绪分析的准确性为60％，使用随机森林 - 随机森林方法是71％。
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