  Sarcasm is considered one of the most difficult problem in sentiment
analysis. In our ob-servation on Indonesian social media, for cer-tain topics,
people tend to criticize something using sarcasm. Here, we proposed two
additional features to detect sarcasm after a common sentiment analysis is
conducted. The features are the negativity information and the number of
interjection words. We also employed translated SentiWordNet in the sentiment
classification. All the classifications were conducted with machine learning
algorithms. The experimental results showed that the additional features are
quite effective in the sarcasm detection.
