Computer Science

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.

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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
[1] ZHAO Y Y,QIN B,LIU T.Text sentiment analysis[J].Journal of Software,2010,21(8):1834-1848.
[2] BARANDELA R,SANCHEZ B J S,GARCIA V,et al.Strategies for learning in class imbalance problems[J].Pattern Recognition,2003,36(3):849-851.
[3] HOCHREITER S,SCHMIDHUBER J.Long Short-Term Me-mory[J].Neural Computation,1997,9(8):1735-1780.
[4] TANG D,QIN B,FENG X,et al.Effective LSTMs for Target-Dependent Sentiment Classification[J].arXiv:1512.01100,2015.
[5] XU F,PAN Z,XIA R.E-commerce product review sentiment classification based on a naïve Bayes continuous learning framework[J].Information Processing & Management,2020,57(5):102221.
[6] MULLEN T,COLLIER N.Sentiment analysis using supportvector machines with diverseinformation sources[C]//Procee-dings of the 2004 Conference on Empirical Methods in Natural Language Processing.2004:412-418.
[7] XIE X,GE S,HU F,et al.An improved algorithm for sentiment analysis based on maximum entropy[J].Soft Computing,2019,23(2):599-611.
[8] PANG B,LEE L,VAITHYANATHAN S.Thumbs up? Sentiment Classification using Machine Learning Techniques[C]//2002 Conference on Empirical Methods in Natural Language Processing.2002:79-86.
[9] JAYANAG B,VINEELA K,VASAVI S.Feature Subsumption for Sentiment Classification of Dynamic Data in Social Networks using SCDDF[J].International Journal of Advanced Computer Science and Applications,2012,3(9):1575-1605.
[10] GRAVES A.Supervised sequence labelling with recurrent neural networks [M].Berlin:Springer,2012.
[11] LONG F,ZHOU K,OU W.Sentiment analysis of text based on bidirectional LSTM with multi-head attention[J].IEEE Access,2019,7:141960-141969
[12] WANG Y,HUANG M,ZHU X,et al.Attention-based LSTM for Aspect-level Sentiment Classification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016.
[13] WU Z,ONG D C.Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis[J].arXiv:2010.07523,2020.
[14] JIANG N,TIAN F,LI J,et al.MAN:Mutual Attention Neural Networks Model for Aspect-Level Sentiment Classification in SIoT[J].IEEE Internet of Things Journal,2020,7(4):2901-2913.
[15] WANG Z H,WANG Z Q,LI S S,et al.Feature Selection for Imbalanced Sentiment Classification[J].Journal of Chinese Information Processing,2013,27(4):113-119.
[16] YE F,JIANG Y S.Unbalanced classification method based on clustering and under-sampling[J].Computer Application and Software,2020,37(1):298-303.
[17] LIN W C.Clustering-based undersampling in class-imbalanced data[J].Information Sciences,2017,409-410:17-26.
[18] LIU X Y,WU J,ZHOU Z H.Exploratory Undersampling for Class-Imbalance Learning[J].IEEE Transactions on Systems Man & Cybernetics Part B,2009,39(2):539-550.
[19] KITTLER J,HATEF M.On combining classifiers[J].IEEETransactions on Pattern Analysis & Machine Intelligence,1998,20(3):226-239.
[20] LI J,LUONG M T,JURAFSKY D,et al.When Are Tree Structures Necessary for Deep Learning of Representations?[C]//The 2015 Conference on Empirical Methods in Natural Language Processing.2015:2304-2314.
[21] BAHDANAU D,CHO K,BENGIO Y.Neural Machine Translation by Jointly Learning to Align and Translate[J].arXiv:1409.0473,2014.
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