Sentiment Classification of Chinese Railway Review Text Based on Multi-Feature Fusion Gated Recurrent Unit
Abstract
Posting comments through online platforms has become the main channel for many users to express their opinions. Many Chinese private companies have rectified their marketing strategies by collecting online user comments and using them as a reference. However, since many Chinese state-owned enterprises are within the system, they can only rectify and reform according to the plan provided by the government but with little effect. China's railway system is faced with such problems. Traditional sentiment classification methods may encounter some problems when processing short texts. Our research provides a Gated Recurrent Unit (GRU) sentiment classification method based on multi-feature fusion and sentiment classification of user reviews about China's railway system. And through classification, we get 1244 comments with negative emotions and 919 non-emotional comments
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