基于不平衡数据与集成学习的属性级情感分类

doi:10.11896/jsjkx.210500205

摘要/Abstract

摘要： 情感分类一直是自然语言处理领域的重要研究部分。该任务一般是将带有情感色彩的样本分类成正类和负类两种类别。在很多理论模型中,都假设正负类数据样本是平衡的,而在现实中正负类样本一般是不平衡的。提出一种基于属性级的LSTM集成学习的方法,针对不平衡样本数据进行属性级情感分类。首先,对数据集进行欠采样处理,将其分成多组;其次,为每组数据分配一种分类算法进行训练;最后,将多组模型融合,得到最终分类结果。一系列的实验结果显示,基于属性级的LSTM集成学习的方法明显提高了分类的准确性,其性能优于传统的LSTM模型分类方法。

关键词: LSTM, 不平衡数据, 集成学习, 情感分类, 属性词

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

中图分类号:

TP391

林夕, 陈孜卓, 王中卿. 基于不平衡数据与集成学习的属性级情感分类[J]. 计算机科学, 2022, 49(6A): 144-149. https://doi.org/10.11896/jsjkx.210500205

LIN Xi, CHEN Zi-zhuo, WANG Zhong-qing. Aspect-level Sentiment Classification Based on Imbalanced Data and Ensemble Learning[J]. Computer Science, 2022, 49(6A): 144-149. https://doi.org/10.11896/jsjkx.210500205

参考文献

[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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