Sentiment Classification of Financial Microblogs through Automatic Text Summarization

With the rapid development of social media, sentiment classification of financial microblogs has been a research hotpot. Online financial microblogs contain a wealth of information on market and usually express emotions or opinions. However, microblogs contain so much noise that it is difficult to make sentiment classification correctly. In this paper, we propose a two-stage model to improve the performance of sentiment classification through automatic text summarization techniques. The automatic text summarization model built on encoder-decoder model as well as attention mechanism can compress the original text and obtain the informative words, which can filter the noise in microblogs and capture significant semantics. Then we build the sentiment classifier based on hierarchical attention network. To make full use of sentiment semantics, we fuse every original blog and its generated summary to train our classifier. We establish two datasets based on Sina Weibo, one is for training the summarization model, another is for sentiment classification. Experimental results indicate that our model outperforms the other baselines on real datasets.