  Knowledge of users' emotion states helps improve human-computer interaction.
In this work, we presented EmoNet, an emotion detector of Chinese daily
dialogues based on deep convolutional neural networks. In order to maintain the
original linguistic features, such as the order, commonly used methods like
segmentation and keywords extraction were not adopted, instead we increased the
depth of CNN and tried to let CNN learn inner linguistic relationships. Our
main contribution is that we presented a new model and a new pipeline which can
be used in multi-language environment to solve sentimental problems.
Experimental results shows EmoNet has a great capacity in learning the emotion
of dialogues and achieves a better result than other state of art detectors do.
