Abstract
Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge. In this paper, we propose a Multi-sentiment-resource Enhanced Attention Network (MEAN) to alleviate the problem by integrating three kinds of sentiment linguistic knowledge (e.g., sentiment lexicon, negation words, intensity words) into the deep neural network via attention mechanisms. By using various types of sentiment resources, MEAN utilizes sentiment-relevant information from different representation subspaces, which makes it more effective to capture the overall semantics of the sentiment, negation and intensity words for sentiment prediction. The experimental results demonstrate that MEAN has robust superiority over strong competitors.
Abstract (translated)
情感分类的深度学习方法并未充分利用情感语言知识。在本文中，我们提出了一种多情感资源增强注意网络（MEAN），通过注意机制将三种情感语言知识（如情感词典，否定词，强度词）整合到深层神经网络中来缓解这一问题。 。通过使用各种类型的情感资源，MEAN利用来自不同表示子空间的情感相关信息，这使得捕获用于情绪预测的情感，否定和强度词的整体语义更加有效。实验结果表明，MEAN比强大的竞争对手具有强大的优势。
URL
https://arxiv.org/abs/1807.04990
