Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 144-149.doi: 10.11896/jsjkx.210500205

• Intelligent Computing • Previous Articles     Next Articles

Aspect-level Sentiment Classification Based on Imbalanced Data and Ensemble Learning

LIN Xi, CHEN Zi-zhuo, WANG Zhong-qing   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:LIN Xi,born in 2000.His main research interests include natural language processing and so on.
    WANG Zhong-qing,born in 1987,Ph.D,is a member of China Computer Federation.His main research interests include natural language processing and sentiment analysis.

Abstract: Sentiment classification remains an important part of the field of natural language processing.The general task is to classify the emotional data into two categories,which is positive and negative.In many models,it is assumed that the positive and negative data are balanced.Contrarily,the two class of data are always imbalanced in reality.This paper proposes an ensemble learning model based on aspect-levelLSTM to process aspect-level problem.Firstly,the data sets are under-sampled and divided into multiple groups.Secondly,a classification algorithm is assigned to each group of data for training.Finally,it yields the classification result through joining all models.The experimental results show that the ensemble learning model based on aspect-level LSTM significantly improves the accuracy of classification,and its performance is better than the traditional LSTM model.

Key words: Aspect word, Ensemble learning, Imbalanced data, LSTM, Sentiment classification

CLC Number: 

  • TP391
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