Sarcasm is a statement that conveys an opposing viewpoint via positive or exaggeratedly positive phrases. Due to this intentional ambiguity, sarcasm identification has become one of the important factors in sentiment analysis that make many researchers in natural language processing intensively study sarcasm detection. This research is using multiple channels embedding the attention bidirectional long-short memory (MCEA-BLSTM) model that explored sarcasm detection in news headlines and has different approach from previous research-developed models that lexical, semantic, and pragmatic properties. This research found that multiple channels embedding attention mechanism improve the performance of BLSTM, making it superior to other models. The proposed method achieves 96.64% accuracy with an f-measure of 97%

Sarcasm is often used to express a negative opinion using positive or intensified positive words in social media. This intentional ambiguity makes sarcasm detection, an important task of sentiment analysis. Detecting a sarcastic tone in natural language hinders the performance of sentiment analysis tasks. The majority of the studies on automatic sarcasm detection emphasize on the use of lexical, syntactic, or pragmatic features that are often unequivocally expressed through figurative literary devices such as words, emoticons, and exclamation marks. In this paper, we introduce a multi-channel attention-based bidirectional long-short memory (MCAB-BLSTM) network to detect sarcastic headline on the news. Multi-channel attention-based bidirectional long-short memory (MCAB-BLSTM) proposed model was evaluated on the news headline dataset, and the results-compared to the CNN-LSTM and Hybrid Neural Network were excellent.