An Effective Sentiment Analysis Model for Tobacco Consumption

Analysis over product reviews has drawn much attention due to its wide application. Most of the sentiment analysis research focuses on entertainment and catering due to the limitation of existing public datasets. In order to promote the comprehensiveness of data in the field of sentiment analysis, we present a new large-scale multi-sentiment tobacco dataset by distilling effective consumer experience information from massive online reviews of tobacco consumption. The release of this dataset would push forward the research in tobacco field. With the goal of advancing and facilitating the research of the overall sentiment of sentences with multiple aspects, we propose simple yet effective EHCRNN model, which combines the strengths of recent NLP advances. Experiments on our new dataset and the public nlpcc2014 task dataset show that the proposed model significantly outperforms the state-of-the-art baseline methods.