Computer Science ›› 2020, Vol. 47 ›› Issue (1): 231-236.doi: 10.11896/jsjkx.181102130

• Artificial Intelligence • Previous Articles     Next Articles

Using SVM Method Optimized by Improved Particle Swarm Optimization to Analyze Emotion of Chinese Text

WANG Li-zhi1,MU Xiao-dong1,LIU Hong-lan2   

  1. (Department of Information Engineering,Rocket Force University of Engineering,Xi’an 710025,China)1;
    (School of Computer & Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China)2
  • Received:2018-11-09 Published:2020-01-19
  • About author:WANG Li-zhi,born in 1994,Ph.D.His main research interests include natural language processing and computer vision;MU Xiao-dong,born in 1965,Ph.D supervisor.His main research interests include intelligent information processing and computer simulation.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61702525).

Abstract: In recent years,with the increasing number of network users,the number of user comments has also increased explosively,accompanied by a large number of information that can be used for reference and deep excavation.Text sentiment classification arises at this historic moment,the prediction accuracy and the execution speed of classification model are the keys to mea-sure the quality of the model.Traditional algorithm by using SVM for text sentiment classification is simple and easy to implement,and its model parameters determine the classification accuracy.In this case,this paper combined the improved particle swarm optimization algorithm with the SVM classification method,used the SVM method optimized by improved particle swarm optimization to analyze the emotion of the movie and TV drama review.Firstly,Douban movie review data are obtained by internet crawler.Then the text information is vectorized by weighted word2vec after pre-processing,which becomes the recognizable input of support vector machine.Adaptive inertia decreasing strategy and crossover operator are used to improve particle swarm optimization algorithm.The loss function,penalty parameter and kernel parameter of SVM model are optimized by improved PSO.Finally,the text is classified by this model.Experimental results on the same data show that this method effectively avoids the shortcomings of traditional affective dictionary method affected by word order and different contexts,and solves the problem of gradient disappearance or dispersion caused by convolution.It also overcomes the possibility that PSO itself is easily trapped in local optimum.Compared with other methods,the proposed classification model performs faster and improves classification accuracy effectively.

Key words: Inertia diminishing, Internet worm, Particle swarm optimization, Sentiment analysis, SVM classification

CLC Number: 

  • TP391
[2]KAUR H,MANGAT V,NIDHI.A survey of sentiment analysis techniques[C]∥International Conference on I-Smac.IEEE,Palladam,India,2017:921-925.
[3]DAVE,KUSHAL,LAWRENCE,et al.Mining the peanut gallery:opinion extraction and semantic classification of product re-views[C]∥Proceedings of the 12th International Conference on World Wide Web.NewYork:ACM,2003.
[4]GO A,BHAYANI R,HUANG L.Twitter sentiment classification using distant supervision[J].Processing,2009,150(12).
[5]JOSHI A,BALAMURALI A R,BHATTACHARYYA P,et al.C-Feel-It:A Sentiment Analyzer for Micro-blogs[C]∥International Conference on Networked Computing & Advanced Information Management.IEEE Computer Society,2008:220-225.
[6]GAMON M,AUE A,CORSTON-OLIVER S,et al.Pulse:mi- ning customer opinions from free text[C]∥International Symposium on Intelligent Data Analysis.Berlin:Springer-Verlag,2005:121-132.
[7]LI S S,HUANG C R,ZHOU G D,et al.Employing personal/impersonal views in supervised and semi-supervised sentiment classification[C]∥Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics.Uppsala:ACL,2010.
[8]LI Y G,ZHOU X G,SUN Y,et al.Research and Implementation of Chinese Microblog Sentiment Classification[J].Journal of Software,2017,28(12):3183-3205.
[9]JOHNSON R,ZHANG T.Effective Use of Word Order for Text Categorization with Convolutional Neural Networks[J].arXiv:1412.1058.
[10]XUE W,LI T.Aspect Based Sentiment Analysis with Gated Convolutional Networks[C]∥Association for Computational Linguistics.Melbourne,Australia,2018:2514-2523.
[11]PARUPALLI S,RAO V A,MAMIDI R.BCSAT:A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations[C]∥Association for Computational Linguistics.Melbourne,Australia,2018:99-104.
[12]ANGELIDIS S,LAPATA M.Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis[C]∥TACL:Transactions of the Association for Computational Linguistics.Melbourne,Australia,2018:17-31.
[13]GUI L,HU J,HE Y,et al.A Question Answering Approach to Emotion Cause Extraction[C]∥Empirical Methods in Natural Language Processing.Copenhagen,Denmark,2017:1593-1602.
[14]YUAN Z,JASON R,DANIEL G,et al.A Fast,Compact,Accurate Model for Language Identification of Codemixed Text [C]∥EMNLP:Empirical Methods in Natural Language Processing.Brussels,Belgium,2018:328-337.
[15]BORDOLOI M,BISWAS S K.Graph-Based Sentiment Analysis Model for E-Commerce Websites’ Data[C]∥CISC:Cognitive Informatics and Soft Computing.Singapore:Springer,2019:453-462.
[16]LI R Y,ZHANG W J,ZHOU Z Y.Improved PSO Algorithm and Its Load Distribution Optimization of Hot Strip Mills[J].Computer Science,2018,45(7):214-218,225.
[17]KENNEDY J.Particle Swarm Optimization[C]∥Icnn95-international Conference on Neural Networks.IEEE,2002.
[18]SHI Y,EBERHART R C.A modified particle swarm optimizer[C]∥Proceedings IEEE Congress on Evolutionary Computation (CEC’98).Anchorage,1998:69-73.
[19]KOU X L.Swarm Intelligence Algorithms and Their Application[D].Xi’an:Xidian University,2009.
[20]RAPAIC' M R,KANOVIC' Ž.Time-varying PSO-convergence analysis,convergence-related parameterization and new parameter adjustment schemes[J].Information Processing Letters,2009,109(11):548-552.
[21]MARTÍNEZ J L F,GARCÍA E.The PSO family:deduction,stochastic analysis and comparison[J].Swarm Intelligence,2009,3(4):245-273.
[22]SHI Y,EBERHART R C.A modified particle swarm optimizer[C]∥Proceedings IEEE Congress on Evolutionary Computation (CEC’98).Anchorage,1998:69-73.
[23]EBERHART R C,SHI Y.Tracking and optimizing dynamic systems with particle swarms[C]∥Congress on Evolutionary Computation.IEEE,2001.
[24]SHI Y,EBERHART R C.Empirical study of particle swarm optimization[C]∥Congress on Evolutionary Computation.Washi-ngton:IEEE,2002.
[25]LIANG J J,QU B Y,SUGANTHAN P N.Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization[R].Technical Report 201311,2013.
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