融合语言特征的卷积神经网络的反讽识别方法

面对某些热点事件,微博评论者经常使用反讽来表达对于该事件的看法,以往的情感分析任务往往忽略这一语言现象。为了提高微博情感分析的准确率,该文对反讽识别开展了研究。通过分析中文文本的语言现象和社交网络的特性,归纳了中文微博反讽的语言特征,提出了一种融合语言特征的卷积神经网络(CNN)的反讽识别方法。该方法将反讽特征和句子分别采用Word Embedding作为输入,再卷积、池化后,将其全连接融合,构建了新的卷积神经网络模型。实验结果表明,该方法在反讽识别的性能上优于传统的基于机器学习的方法。

Linguistic Features Enhanced Convolutional Neural Networks for Irony Recognition

Irony is popular in Weibo comments, which is less addressed in past sentiment analysis community. To improve the accuracy of Weibo sentiment analysis, we study irony recognition in this paper. By analyzing the characteristics of Chinese language and social networks, irony linguistic features are summarized. Combining these linguistic features with convolutional neural networks (CNN), a novel method is proposed for recognizing irony. The proposed method combines irony features representation and sentences representation using word embedding as the input of a convolution network. The experimental results indicate that the proposed method is superior to classical machine learning methods for irony recognition.