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

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)
[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)
[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)
[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)
[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].
[1] WANG Han, XIA Hong-bin. Collaborative Filtering Recommendation Algorithm Mixing LDA Model and List-wise Model [J]. Computer Science, 2019, 46(9): 216-222.
[2] JU Ya-ya, YANG Lu, YAN Jian-feng. LDA Algorithm Based on Dynamic Weight [J]. Computer Science, 2019, 46(8): 260-265.
[3] SHEN Chen-lin, ZHANG Lu, WU Liang-qing, LI Shou-shan. Sentiment Classification Towards Question-Answering Based on Bidirectional Attention Mechanism [J]. Computer Science, 2019, 46(7): 151-156.
[4] HAN Hui,WANG Li-ming,CHAI Yu-mei,LIU Zhen. Text Sentiment Classification Based on Deep Forests with Enhanced Features [J]. Computer Science, 2019, 46(7): 172-179.
[5] LIU Chang-yun,YANG Yu-di,ZHOU Li-hua,ZHAO Li-hong. Discovering Popular Social Location with Time Label [J]. Computer Science, 2019, 46(7): 186-194.
[6] ZHANG Lei,CAI Ming. Image Annotation Based on Topic Fusion and Frequent Patterns Mining [J]. Computer Science, 2019, 46(7): 246-251.
[7] PENG Cheng, HE Jing, CHI Hao. Boundary Distance Algorithm for Determining Sliding Window Size [J]. Computer Science, 2019, 46(6A): 482-487.
[8] ZHANG Shuai, FU Xiang-ling, HOU Yi. Prediction Model of P2P Trading Volume Based on Investor Sentiment [J]. Computer Science, 2019, 46(6A): 60-65.
[9] ZHANG Wei-guo. Decision Making of Course Selection Oriented by Knowledge Recommendation Service [J]. Computer Science, 2019, 46(6A): 507-510.
[10] FU Ze-qiang, WANG Xiao-feng, KONG Jun. High-performance Association Analysis Method for Network Security Alarm Information [J]. Computer Science, 2019, 46(5): 116-121.
[11] LI Wen-hai, CHENG Jia-yu, XIE Chen-yang. Prediction Method of Cyclic Time Series Based on DTW Similarity [J]. Computer Science, 2019, 46(5): 157-162.
[12] ZHANG Fang, ZHAO Shu-liang, WU Yong-liang. Data Scaling Method for Multi-scale Data Mining [J]. Computer Science, 2019, 46(4): 57-65.
[13] FENG Yun-fei, CHEN Hong-mei. Topological Structure Based Density Peak Algorithm for Overlapping Community Detection [J]. Computer Science, 2019, 46(10): 39-48.
[14] YU Yuan-yuan, CHAO Wen-han, HE Yue-ying, LI Zhou-jun. Cross-language Knowledge Linking Based on Bilingual Topic Model and Bilingual Embedding [J]. Computer Science, 2019, 46(1): 238-244.
[15] ZHANG Xiao-chuan, YU Lin-feng, ZHANG Yi-hao. Multi-feature Fusion for Short Text Similarity Calculation Based on LDA [J]. Computer Science, 2018, 45(9): 266-270.
Full text



[1] . [J]. Computer Science, 2018, 1(1): 1 .
[2] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[3] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[4] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[5] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[6] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[7] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[8] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[9] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[10] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .