Shih-Kai Lin

Based on the assumption that comment with positive sentimental polarity to a negative issue has high probability to be a sarcasm, we propose a simple yet efficient method to collect sarcastic textual data by crowdsourcing with social media and merging game with a purpose approach. Taking advantage of Facebook's reaction button, posts triggering strong negative emotion are collected. Next, by using PTT's search engine, we successfully connect PTT's comments to the collected posts in Facebook and build the sarcasm corpus. Based on the corpus data, the performance comparison of sarcasm detection between SVM with naïve features and Convolutional Neural Network models is conducted. An impressive accuracy rate and great potentials of the corpus are demonstrated.

Based on the assumption that comment with positive sentimental polarity to a negative issue has high probability to be a sarcasm, we propose a simple yet efficient method to collect sarcastic textual data by crowdsourcing with social media and merging game with a purpose approach. Taking advantage of Facebook's reaction button, posts triggering strong negative emotion are collected. Next, by using PTT's search engine, we successfully connect PTT's comments to the collected posts in Facebook and build the sarcasm corpus. Based on the corpus data, the performance comparison of sarcasm detection between SVM with naïve features and Convolutional Neural Network models is conducted. An impressive accuracy rate and great potentials of the corpus are demonstrated.