Hybrid attention-based Long Short-Term Memory network for sarcasm identification

Abstract Sentiment analysis of people’s opinion is used in a lot of business and decision-making scenarios. Although social media is an informal medium in which to express one’s opinions, it is being used in many business and decision-making scenarios now. Social media posts contain a lot of sarcastic statements that affect the automatic extraction of the correct sentiment of the post, as sarcasm can flip the overall polarity of the sentence. Sarcasm is a bitterly cutting form of irony to be unpleasant to somebody or to make fun of them. Therefore, identifying sarcastic statements from the users’ posts has become an important task to extract the actual sentiments from informal statements regarding an event or a person. In this work, we propose a hybrid attention-based Long Short Term Memory (HA-LSTM) network to identify sarcastic statements. This HA-LSTM network is different than the existing LSTM model, as the proposed HA-LSTM network combines 16 different linguistic features in their hidden layers. The proposed HA-LSTM network is validated with three benchmark datasets. The combination of 16 different linguistic features shows an improvement in the performance of the model in comparison with other state-of-the-art models with an improvement of up to 2% in terms of F 1 -score with three different gold standard datasets.