Sentiment Classification with Convolutional Neural Networks: An Experimental Study on a Large Scale Chinese Conversation Corpus
Previous research on sentiment classification by machine learning algorithms has shown that they usually work well with large scale dataset. However, most open datasets for Chinese sentiment classification are quite small. In this paper we build a large scale annotated Chinese sentiment dataset by filtering a vast amount of human computer conversations. We conduct thorough experiments by using Convolutional Neural Networks (CNNs) and other classical machine learning methods. The experimental evaluation is made on a human annotated dataset and COAE2014 task. The extensive experiments demonstrate that Chinese sentiment classification task can benefit from our dataset and CNNs model can achieve better performance than classical machine learning approaches.
2016 12th International Conference on Computational Intelligence and Security (CIS)