Affiliations: [a]
School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China
| [b]
School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
| [c]
Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing, China
Correspondence:
[*]
Corresponding author. Yang Xuexin, School of Mechanical-electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, 100044, Beijing, China. Tel.: +86 19801369012; E-mail: [email protected].
Abstract: The purpose of sentiment classification is to accomplish automatic judssssgment of the sentiment tendency of text. In the sentiment classification task of online reviews, traditional models focus on the optimization of algorithm performance, but ignore the imbalanced distribution of the number of sentiment classifications of online reviews, which causes serious degradation in the classification performance of the model in practical applications. The experiment was divided into two stages in the overall context. The first stage trains SimBERT using online review data so that SimBERT can fully learn the semantic features of online reviews. The second stage uses the trained SimBERT model to generate fake minority samples and mix them with the original samples to obtain a distributed balanced dataset. Then the mixed data set is input into the deep learning model to complete the sentiment classification task. Experimental results show that this method has excellent classification performance in the sentiment classification task of hotel online reviews compared with traditional deep learning models and models based on other imbalanced processing methods.
Keywords: Sentiment classification, imbalance classification, deep learning, BERT, SimBERT
