Indonesian Social Media Sentiment Analysis With Sarcasm Detection
This addresses the problem of sarcasm detection for sentiment analysis in Indonesian social media, which is an incremental improvement.
The paper tackled sarcasm detection in Indonesian social media sentiment analysis by proposing two additional features (negativity information and interjection word count) and using translated SentiWordNet with machine learning algorithms, resulting in quite effective detection as shown in experimental results.
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.