Computer Science ›› 2018, Vol. 45 ›› Issue (12): 142-147.doi: 10.11896/j.issn.1002-137X.2018.12.022

• Artificial Intelligence • Previous Articles     Next Articles

Study on Recursive Auto-encoding Sentiment Classification Based on Topic Enhancement

ZHU Yin, HUANG Hai-yan   

  1. (School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)
  • Received:2017-11-07 Online:2018-12-15 Published:2019-02-25

Abstract: The emotional analysis of Chinese text aims to discover the emotional tendencies of users to things and events,however,the existing studies often neglect the interrelationships between texts.In light of this,this paper proposed a recursive auto-encoding classification model based on topic enhancement.By incorporating the subject information of the text into the recursive auto-encoding model,this model can further consider the content information of the text and improve the capability to understand the text emotion and generaliza ability.The experimental results on the COAE2014 dataset show that the proposed classification model can achieve better classification performance when used for tasks of sentiment classification,thus verifying its applicability and feasibility in practical problems.

Key words: Data mining, Recursive auto-encoder, Sentiment classification, Topic model

CLC Number: 

  • TP391
[1]ZHANG Z Q,YE Q,LI Y J,et al.Literature review on sentiment analysis of online product reviews [J].Journal of Management Sciences in China,2010,13(6):84-96.(in Chinese)
张紫琼,叶强,李一军,等.互联网商品评论情感分析研究综述[J].管理科学学报,2010,13(6):84-96.
[2]赵军,许洪波,黄萱菁.中文倾向性分析评测技术报告[EB/OL].http://www.doc88.com/p-179806395884.html.
[3]BO P,LEE L.Seeing stars:exploiting class relationships for sentiment categorization with respect to rating scales[C]∥Meeting on Association for Computational Linguistics.2005:115-124.
[4]ZHOU S C,QU W T,SHI Y Z,et al.Overview on sentiment analysis of Chinese microblogging[J].Computer Applications and Software,2013,30(3):161-164.(in Chinese)
周胜臣,瞿文婷,石英子,等.中文微博情感分析研究综述[J].计算机应用与软件,2013,30(3):161-164.
[5]TURNEY P D.Thumbs up or thumbs down:semantic orientation applied to unsupervised classification of reviews[C]∥Mee-ting on Association for Computational Linguistics.2002:417-424.
[6]LUO Y,LI L,TAN S B,et al.Sentiment analysis on Chinese Micro-blog corpus[J].Journal of Shandong University (Natural Science),2014,49(11):1-7.(in Chinese)
罗毅,李利,谭松波,等.基于中文微博语料的情感倾向性分析[J].山东大学学报(理学版),2014,49(11):1-7.
[7]WANG S,MANNING C D.Baselines and bigrams:simple,good sentiment and topic classification[C]∥Meeting of the Association for Computational Linguistics:Short Papers.Association for Computational Linguistics,2012:90-94.
[8]SOCHER R,PENNINGTON J,HUANG E H,et al.Semi-supervised recursive auto-encoders for predicting sentiment distributions[C]∥Conference on Empirical Methods in Natural Language Processing.DBLP,2011:151-161.
[9]TANG D,QIN B,LIU T.Document Modeling with Gated Recurrent Neural Network for Sentiment Classification[C]∥Conference on Empirical Methods in Natural Language Processing.2015:1422-1432.
[10]ZHANG L,CHEN C.Sentiment Classification with Convolu-tional Neural Networks:An Experimental Study on a Large-Scale Chinese Conversation Corpus[C]∥InternationalConfe-rence on Computational Intelligence and Security.IEEE,2017:165-169.
[11]LIANG J,CHAI Y M,YUAN H B,et al.Deep Learning for Chinese Micro-blog Sentiment Analysis[J].Journal of Chinese Information Processing,2014,28(5):155-161.(in Chinese)
梁军,柴玉梅,原慧斌,等.基于深度学习的微博情感分析[J].中文信息学报,2014,28(5):155-161.
[12]TANG D,WEI F,YANG N,et al.Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification[C]∥Meeting of the Association for Computational Linguistics.2014:1555-1565.
[13]LEVY O,GOLDBERG Y.Neural word embedding as implicit matrix factorization[J].Advances in Neural Information Processing Systems,2014,3(4):2177-2185.
[14]BLEI D M,NG A Y,JORDAN M I.Latent dirichletallocation[J].Journal of Machine Learning Research,2003,3(6):993-1022.
[15]GRIFFITHS T L,STEYVERS M.Finding scientific topics[J].Proceedings of the National Academy of Sciences of the United States of America,2004,1011(1):5228.
[16]ZHANG S,ZHANG C,YOU Z,et al.Asynchronous stochastic gradient descent for DNN training[C]∥IEEE International Conference on Acoustics,Speech and Signal Processing.IEEE,2013:6660-6663.
[17]XIAO H,MAO M Y.NLPIR Chinese character segmentationsystem based Chinese character segmentation tool:CN 106354714 A[P].2017.(in Chinese)
肖红,毛明扬.一种基于nlpir中文分词系统的中文分词工具:CN 106354714 A[P].2017.
[18]ORDENTLICH E,YANG L,FENG A,et al.Network-Efficient Distributed Word2vec Training System for Large Vocabularies[J/OL].https://arxiv.org.abs/606.08495.
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