随着社交媒体的发展,越来越多的人在社交平台上发表对热点话题的看法,其中讽刺手法的运用严重影响了社交媒体中情感分析的精度.目前面向话题的讽刺识别研究未同时考虑上下文和常识知识的作用,也忽略了在同一个话题下进行讽刺识别的场景.为此,提出了基 于上下文和常识的 讽刺识别模型(Sarcasm Detection with Context and Common Sense,CCSD).首先,模型使用C3KG常识库生成常识文本,并将目标句、话题上下文和常识文本作为预训练BERT模型的输入.其次,使用注意力机制来关注目标句和常识中重要的信息.最后,通过门控机制和特征融合,实现讽刺识别.文中构建了一个面向话题的讽刺识别数据集,以验证模型在特定话题中的有效性.实验结果表明,相比基线模型,新模型的性能更优.
CCSD:Topic-oriented Sarcasm Detection
With the development of social media,an increasing number of people express their opinions about hot topics on social platforms,and the utilization of sarcastic expression has severely affected the accuracy of sentiment analysis in social media.Cur-rently,topic-oriented sarcasm detection research does not consider the role of context and common sense knowledge simultaneous-ly,and also ignores the scene of sarcasm recognition under the same topic.This paper proposes a sarcasm detection with context and commonsense(CCSD)approach.Firstly,the model uses the C3 KG commonsense knowledge base to generate commonsense text.Then,the target sentence,topic context,and commonsense text are concatenated as the input to the pre-training BERT model.In addition,an attention mechanism is used to focus on important information in the target sentence and commonsense text.Finally,sarcasm detections are realized through gating mechanism and feature fusion.A topic-oriented sarcasm detection dataset is constructed to verify the effectiveness of the proposed model in specific topics.Experimental results show that the pro-posed model achieves better performance compared to baseline models.
Sarcasm detectionTopic-oriented sarcasm detectionContextCommon sense knowledgeAttention mechanism
万方数据
维普
