Multi-view Opinion Mining with Deep Learning

With the explosive growth of social media on the Internet, people are expressing an increasing number of opinions. As for objectives like business decision making and public opinion analysis, how to make the best of these precious opinionated words is a new challenge in the field of NLP. The field of opinion mining, or sentiment analysis, has become active in recent years. Since different kinds of deep neural networks differ in their structures, they are probably extracting different features. We investigated whether features generated by heterogeneous deep neural networks can be combined by multi-view learning to improve the overall performance. With document level opinion mining being the objective, we implemented multi-view learning based on heterogeneous deep neural networks. Experiments show that multi-view learning utilizing these heterogeneous features outperforms single-view deep neural networks. Our framework makes better use of single-view data.