基于熵值法改进Stacking的文本情感分析  

Improved Stacking of Text Emotion Analysis Based on Entropy Method

作　　者：刘甜甜[1] 谷晓燕[1] 陈梦彤 LIU Tian-tian;GU Xiao-yan;CHEN Meng-tong(School of Information Management,Beijing Information Science&Technology University,Beijing 100192,China)

机构地区：[1]北京信息科技大学信息管理学院,北京100192

出　　处：《科学技术与工程》2023年第23期10008-10014,共7页Science Technology and Engineering

基　　金：国家自然科学基金(71701020);国家重点研发计划(2019YFB1405003)。

摘　　要：在情感分析研究中,使用Stacking算法进行情感分析时基学习器的选择是至关重要的。传统的Stacking算法仅仅只是将不同学习器结合起来,没有区分它们之间的不同,同时也不能反映初级学习器的实际预测情况,针对此问题,基于熵值法改进Stacking算法进行文本的情感分类。首先,使用熵值法确定单一分类器的性能指标权重,将指标值的权重进行加权求和获得不同模型的综合得分,通过综合得分来选择性能最好的基学习器组合;接着,由于基模型中的各个分类器性能的不同,将基学习器训练后的预测结果赋予不同的权重,输入到次级学习器当中;最后再利用次级学习器进行训练并预测情感倾向。实验结果表明,基于熵值法改进Stacking模型优于传统的Stacking模型,说明基学习器的选择和重要程度对情感分类具有一定帮助,为之后文本情感分析奠定一定的基础。In the research of emotion analysis,it is very important to use the Stacking algorithm to select the time base learners for emotion analysis.The traditional Stacking algorithm only combines different learners,does not distinguish the differences between them,and can not reflect the actual prediction of the primary learners.To solve this problem,the Stacking algorithm based on entropy method was improved to classify text emotion.First,the entropy method was used to determine the performance index weight of a single classifier.The weight of the index value was weighted and summed to obtain the comprehensive score of different models.The best combination of base learners was selected through the comprehensive score.Then,due to the different performance of each classifier in the base model,the prediction results after the training of the base learner were given different weights and input into the secondary learner.Finally,secondary learners were used to train and predict emotional tendencies.The experimental results show that the im-proved Stacking model based on entropy method is superior to the traditional Stacking model,and the selection and importance of the base learners have certain help for emotion classification,which lays a certain foundation for later text emotion analysis.

关 键 词：情感分析 熵值法 基分类器选择 改进Stacking 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]