基于不平衡数据与集成学习的属性级情感分类  

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

作　　者：林夕 陈孜卓 王中卿[1] LIN Xi;CHEN Zi-zhuo;WANG Zhong-qing(School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China)

机构地区：[1]苏州大学计算机科学与技术学院,江苏苏州215006

出　　处：《计算机科学》2022年第S01期144-149,共6页Computer Science

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。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.

关 键 词：不平衡数据 LSTM 集成学习 情感分类 属性词 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]