A Novel Attention Based CNN Model for Emotion Intensity Prediction.

Recently, classifying sentiment polarities or emotion categories of social media text has drawn extensive attentions from both academic and industrial communities. However, limited efforts have been paid for emotion intensity prediction problem. In this paper, we propose a novel attention mechanism for CNN model that associates attention based weights for every convolution window. Furthermore, a new activation function is incorporated into the full-connected layer, which can alleviate the small gradient problem in function's saturated region. Experiment results on benchmark dataset show that our proposed model outperforms several strong baselines and achieves comparable performance with the state-of-the-art models. Unlike the reported models that used different neural network architectures for different emotion categories, our proposed model utilizes a unified architecture for intensity prediction.