Tweet Irony Detection Using Ensembles of Word Level Attentive Long Short term Memory and Convolutional Neural Network
Irony detection is a key subtask of many natural language processing tasks. For example, irony can flip the polarity of "apparently positive" sentences and have a negative impact on polarity detection performance in sentiment analysis. Up to now, most of the irony detection methods are simple to deal with the task as text classification. However, irony expresses semantics in a very subtle way, so it requires a deeper understanding of natural language, which is beyond the grasp of standard text categorization techniques. In this paper, we design and ensemble two independent models, based on Bidirectional Long Short term Memory(BiLSTM) strengthened with self attention mechanism and a pre trained Convolutional Neural Network(CNN) to capture both the semantic and sentiment information in tweets. In the test set of irony detection, our model is superior to the traditional approach in terms of F1 score. More importantly, the model training takes less time because we don't need to manually find the rules and prepare a lot of time consuming and labor intensive other language knowledge, so our model is more developable.
2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC FSKD)