An irony detection model for social media comments based on topic context

Due to the ever-expanding amount of information available on social media comments, the need for reliable and efficient irony detection mechanisms becomes evident. However, most current methods focus on analysing the comment text, ignoring the role of the topic context in promoting the understanding of ironic semantics. In order to provide a more efficient framework for extracting ironic text features, we propose an irony detection model for social media comments based on topic context (SMC-TC). The model is mainly composed of the information union module and the sentiment-guided module. In the information union module, the topic-comment text features are obtained through Bi-GRU and attention-over-attention (AOA) to realise the combination of topic context and comments. In the sentiment-guided module, the comment word embeddings are input into LSTM and combined with the self-attention mechanism to get the sentimental semantic features. Based on the above two modules, the sentimental semantic features and topic-comment text features are concatenated and then fed into a sigmoid function to acquire the final irony detection result. Through extensive experiments on the publicly available 'ToSarcasm dataset', our model can enhance the F1-score to 74.04%.