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.

PDF (PC)

508

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.
[1] ZHANG Yuan, KANG Le, GONG Zhao-hui, ZHANG Zhi-hong. Related Transaction Behavior Detection in Futures Market Based on Bi-LSTM [J]. Computer Science, 2022, 49(7): 31-39.
[2] JIN Fang-yan, WANG Xiu-li. Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM [J]. Computer Science, 2022, 49(7): 179-186.
[3] KANG Yan, WU Zhi-wei, KOU Yong-qi, ZHANG Lan, XIE Si-yu, LI Hao. Deep Integrated Learning Software Requirement Classification Fusing Bert and Graph Convolution [J]. Computer Science, 2022, 49(6A): 150-158.
[4] WANG Shan, XU Chu-yi, SHI Chun-xiang, ZHANG Ying. Study on Cloud Classification Method of Satellite Cloud Images Based on CNN-LSTM [J]. Computer Science, 2022, 49(6A): 675-679.
[5] WANG Yu-fei, CHEN Wen. Tri-training Algorithm Based on DECORATE Ensemble Learning and Credibility Assessment [J]. Computer Science, 2022, 49(6): 127-133.
[6] HAN Hong-qi, RAN Ya-xin, ZHANG Yun-liang, GUI Jie, GAO Xiong, YI Meng-lin. Study on Cross-media Information Retrieval Based on Common Subspace Classification Learning [J]. Computer Science, 2022, 49(5): 33-42.
[7] DONG Qi-da, WANG Zhe, WU Song-yang. Feature Fusion Framework Combining Attention Mechanism and Geometric Information [J]. Computer Science, 2022, 49(5): 129-134.
[8] PAN Zhi-hao, ZENG Bi, LIAO Wen-xiong, WEI Peng-fei, WEN Song. Interactive Attention Graph Convolutional Networks for Aspect-based Sentiment Classification [J]. Computer Science, 2022, 49(3): 294-300.
[9] DING Feng, SUN Xiao. Negative-emotion Opinion Target Extraction Based on Attention and BiLSTM-CRF [J]. Computer Science, 2022, 49(2): 223-230.
[10] REN Shou-peng, LI Jin, WANG Jing-ru, YUE Kun. Ensemble Regression Decision Trees-based lncRNA-disease Association Prediction [J]. Computer Science, 2022, 49(2): 265-271.
[11] CHEN Wei, LI Hang, LI Wei-hua. Ensemble Learning Method for Nucleosome Localization Prediction [J]. Computer Science, 2022, 49(2): 285-291.
[12] JIANG Hao-chen, WEI Zi-qi, LIU Lin, CHEN Jun. Imbalanced Data Classification:A Survey and Experiments in Medical Domain [J]. Computer Science, 2022, 49(1): 80-88.
[13] LIU Zhen-yu, SONG Xiao-ying. Multivariate Regression Forest for Categorical Attribute Data [J]. Computer Science, 2022, 49(1): 108-114.
[14] YUAN Jing-ling, DING Yuan-yuan, SHENG De-ming, LI Lin. Image-Text Sentiment Analysis Model Based on Visual Aspect Attention [J]. Computer Science, 2022, 49(1): 219-224.
[15] ZHOU Xin-min, HU Yi-gui, LIU Wen-jie, SUN Rong-jun. Research on Urban Function Recognition Based on Multi-modal and Multi-level Data Fusion Method [J]. Computer Science, 2021, 48(9): 50-58.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.