基于RS-SVM的网络商品评论情感分析研究

计算机科学 ›› 2013, Vol. 40 ›› Issue (Z11): 274-277.

基于RS-SVM的网络商品评论情感分析研究

王刚,杨善林

合肥工业大学管理学院合肥230009过程优化与智能决策教育部重点实验室合肥230009;合肥工业大学管理学院合肥230009过程优化与智能决策教育部重点实验室合肥230009

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受国家自然科学基金(71101042),高等学校博士学科点专项科研基金(20110111120014),中国博士后科学基金(2011M501041,3T60611),国家重点基础研究发展计划(973计划)(2013CB329603),合肥工业大学政治理论研究中心课题(2012HGXJ0392)资助

Study of Sentiment Analysis of Product Reviews in Internet Based on RS-SVM

WANG Gang and YANG Shan-lin

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 网络商品评论情感分析对网络购物用户的决策有着重要的帮助,因此,分类准确性的提高一直是网络商品评论情感分析研究关注的重点问题之一。近些年,集成学习理论是提高分类精度的一种有效途径,并已有研究将Bagging、Boosting引入网络商品评论的情感分析领域,但对于Random Subspace集成学习方法关注相对较少。为此,本研究根据网络商品评论情感分析问题的高维度数据特征,提出一个新的网络商品评论情感分析方法RS-SVM。该方法以集成学习中的Random Subspace为基础,选取目前在情感分析领域广泛应用的SVM作为基学习器,通过集成Random Subspace较强的学习能力,进一步提高网络用户评论情感分析的准确程度。最后,在网络商品评论情感分析经典数据库Movie Reviews上进行了实验,结果表明RS-SVM取得了比其它分类器都好的实验结果。

关键词: 情感分析,商品评论,集成学习,Random Subspace,SVM

Abstract: As product reviews in the internet are helpful for the decision of online shopping,and the classification accuracy of sentiment analysis is one of important problems．Recently,ensemble learning has been proved to be an effective method of enhancing the classification accuracy．Bagging and Boosting have been applied into the sentiment analysis,while Random Subspace is paid less attention to．In this paper,an new method,RS-SVM,was proposed for sentiment analysis based on the characteristic of high dimension of product review''s dataset．RS-SVM uses the state-of-the-art SVM as base learner and Random Subspace as ensemble method in order to enhance the accuracy of sentiment analysis．Lastly,experiments based on movie reviews'' dataset were conducted to verify the effectiveness of RS-SVM．Experimental results reveal that RS-SVM gets the best classification results compared with other methods.

Key words: Sentiment analysis,Production review,Ensemble learning,Random subspace,SVM

王刚,杨善林. 基于RS-SVM的网络商品评论情感分析研究[J]. 计算机科学, 2013, 40(Z11): 274-277. https://doi.org/

WANG Gang and YANG Shan-lin. Study of Sentiment Analysis of Product Reviews in Internet Based on RS-SVM[J]. Computer Science, 2013, 40(Z11): 274-277. https://doi.org/

参考文献

[1] 张紫琼,叶强,李一军.互联网商品评论情感分析研究综述[J]．管理科学学报,2010,13(006):84-96
[2] Pang B,Lee L．Opinion mining and sentiment analysis [J]．Foundations and trends in information retrieval,2008,2(1/2):1-135
[3] 赵妍妍,秦兵,刘挺.文本情感分析[J]．软件学报,2010,21(8):1834-1848
[4] Polikar R．Ensemble based systems in decision making [J]．Circuits and Systems Magazine,IEEE,2006,6(3):21-45
[5] Dietterich T．Ensemble methods in machine learning [J]．Multiple classifier systems,2000:1-15
[6] Vapnik V N．The nature of statistical learning theory [M]．Springer Verlag,2000
[7] Dasarathy B V,Sheela B V．A composite classifier system design:concepts and methodology [J]．Proceedings of the IEEE,1979,67(5):708-713
[8] Hansen L K,Salamon P．Neural network ensembles [J]．IEEETransactions on Pattern Analysis and Machine Intelligence,1990,12(10):993-1001
[9] Schapire R E,Freund Y,Bartlett P,et al.．Boosting the margin:A new explanation for the effectiveness of voting methods [J]．The annals of statistics,1998,26(5):1651-1686
[10] Breiman L．Bagging predictors [J]．Machine Learning,1996,24(2):123-140
[11] Freund Y,Schapire R E．Experiments with a new boosting algorithm [M]．Morgan Kaufmann Publishers,Inc,1996
[12] Ho T K．The random subspace method for constructing decision forests [J]．IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(8):832-844

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