关键词: 情感分析, 读者情绪预测, 卷积神经网络, 门限循环神经网络, 注意力机制

Abstract: The past reader’s emotion prediction methods are unable to capture the complex semantic and grammatical information in the document, and have mostly used in multi-label classification technology, which limit its development and application. To solve this problem, an improved method named attention-based convolutional-gated recurrent neural network for reader’s emotion prediction is presented. This method firstly divides the document into several sentences, and then adopts convolutional neural network to produce sentence-level representations from word vectors. Such useful sentence features can be sequentially integrated using gated recurrent neural network, and a novel attention mechanism is proposed to build a document-level representation according to their contribution to reader’s emotion prediction. Finally, a softmax regression is applied to predict reader’s emotion distributions. Experimental results on Yahoo news corpus demonstrate that the proposed method achieves better accuracy compared with state-of-the-art methods.

Key words: sentiment analysis, reader’s emotion prediction, convolutional neural network, gated recurrent neural network, attention mechanism