Indonesian social media sentiment analysis with sarcasm detection

Published 1 September 2013Open access

Edwin Lunando, Ayu Purwarianti

DOI10.1109/icacsis.2013.6761575

Citations127

TL;DR

This work 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.

Abstract

Sarcasm is considered one of the most difficult problem in sentiment analysis. In our observation on Indonesian social media, for certain 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.

Keywords

Computer Science

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