Computer Science ›› 2020, Vol. 47 ›› Issue (1): 186-192.doi: 10.11896/jsjkx.181002011

• Artificial Intelligence • Previous Articles     Next Articles

Comment Sentiment Analysis and Sentiment Words Detection Based on Attention Mechanism

LI Yuan,LI Zhi-xing,TENG Lei,WANG Hua-ming,WANG Guo-yin   

  1. (Chongqing Key Lab of Computation Intelligence,Chongqing 400065,China)
  • Received:2018-10-31 Published:2020-01-19
  • About author:LI Yuan,born in 1992,master.Her main research interests include deep learning and network security;WANG Guo-yin,born in 1970,Ph.D,professor,Ph.D supervisor.His main research interests include rough set,granular computing,data mining and machine learning.
  • Supported by:
    This work was supported by the National Key R&D Program of China (2017YFB0802300),National Natural Science Foundation of China (61502066) and Chongqing Basic and Frontier Research Project (cstc2015jcyjA40018).

Abstract: Comment sentiment analysis is one of the research hotspots in user generated content field.Because of the diversity of comment objects and the casualness of commentators’ language,comment sentiment analysis has become a challenging issue.The existing methods mainly calculate the emotional polarity of comments by pre-building the emotional vocabulary.However,these methods cannot adapt to the problem that the same words have different emotional polarities in different contexts.To overcome this problem,the attention based convolutional-recurrent neural network (A-CRNN) model was proposed to model the emotional polarity of comments and words in different contexts.By combining the context of words in sentences,the proposed method can focus attention on a small scale around the main emotional words.The A-CRNN model calculates the emotional polarity of the words through an adaptive method,which improves the accuracy of words’ emotional polarity judgment and the accuracy of short texts’ emotional polarity.Compared with CRNN,CNN and emotional dictionary methods,the proposed method achieves better results in Chinese dataset induding Meituan Review,Party Building Review and English dataset including Amazon Product Review.

Key words: Attention mechanism, Convolutional-recurrent neural network, Emotional analysis, Multi-granularity

CLC Number: 

  • TP391
[1]LIU C Y,RAN Q.Attitudinal Resources in Customers' Remarks in CSC and Their Impacts on Potential Customers’ Purchase Decisions[J].Journal of University of Science and Technology Beijing(Social Sciences Edition),2017,6(33):1-7.
[2]JHA V,SAVITHA R,SHENOY P D,et al.A novel sentiment aware dictionary for multi-domain sentiment classification[J].Computers & Electrical Engineering,2018,69:585-597.
[3]LIU F,WEI F,YU K,et al.Sentiment Classification of Reviews on Automobile Websites by Combining Word2Vec and Depen-dency Parsing[C]∥Proceedings of the International Conference on Smart Computing and Communication.Springer,Cham,2017:206-221.
[4]TRIPATHY A,RATH S K.Classification of sentiment of re- views using supervised machine learning techniques[J].International Journal of Rough Sets and Data Analysis (IJRSDA),2017,4(1):56-74.
[5]MANEK A S,SHENOY P D,MOHAN M C,et al.Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier[J].World Wide Web,2017,20(2):135-154.
[6]YORDANOVA S,KABAKCHIEVA D.Sentiment Classification of Hotel Reviews in Social Media with Decision Tree Learning[J].International Journal of Computer Applications,2017,158(5):0975-8887.
[7]TRIPATHI G,NAGANNA S.Feature selection and classification approach for sentiment analysis[J].Machine Learning and Applications:An International Journal,2015,2(2):1-16.
[8]BILAL M,ISRAR H,SHAHID M,et al.Sentiment classification of Roman-Urdu opinions using Naïve Bayesian,Decision Tree and KNN classification techniques[J].Journal of King Saud University-Computer and Information Sciences,2016,28(3):330-344.
[9]LI N,ZHAI S,ZHANG Z,et al.Structural Correspondence Learning for Cross-Lingual Sentiment Classification with One-to-Many Mappings[C]∥Thirty-First AAAI Conference on Artificial Intelligence.San Francisco,USA,2017:3490-3496.
[10]GEHRING J,AULI M,GRANGIER D,et al.Convolutional Sequence to Sequence Learning[C]∥Proceedings of the International Conference on Machine Learning.Sydney,NSW,2017:1243-1252.
[11]HE H,GIMPEL K,LIN J.Multi-perspective sentence similarity modeling with convolutionalneural networks[C]∥Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.Doha,Qatar,2015:1576-1586.
[12]ZENG D,LIU K,CHEN Y,et al.Distant supervision for relation extraction via piecewise convolutional neural networks[C]∥Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.Doha,Qatar,2015:1753-1762.
[13]KIM Y.Convolutional Neural Networks for Sentence Classification[C]∥Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).Doha,Qatar,2014:1746-1751.
[14]ZHANG Y,WALLACE B.A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification[C]∥Proceedings of the Eighth Internatio-nal Joint Conference on Natural Language Processing.Taipei,China,2017,1:253-263.
[15]ZHANG X,ZHAO J,LECUN Y.Character-level convolutional networks for text classification[C]∥ Processing of the 2015 Neural Information Processing Systems.San Diego,USA,Neural Information Processing Systems Foundation,2015:649-657.
[16]CONNEAU A,SCHWENK H,BARRAULT L,et al.Very deep convolutional networks for text classification[C]∥ Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics.Valencia,Spain:Association for Computational Linguistics,2017:1107-1116.
[17]GUO H.A Deep Network with Visual Text Composition Beha- vior[C]∥Proceedings of the 55th Annual Meeting of the Asso-ciation for Computational Linguistics.Vancouver,Canada,2017,2:372-377.
[18]LI J,LI H.Research on Product Feature Extraction and Sentiment Classification of Short Online Review Based on Deep Learning[J].Information Studies:Theory & Application,2018,2:26.
[19]XU Y Y.Research of Sentence-Level Sentiment Classification for Text Based on Deep Neural Network[D].Shenzhen:Shenzhen University,2016.
[20]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[C]∥ Proceedings of the International Conference on Learning Representations.Scottsdale,Arizona,2013:1-12.
[21]MCAULEY J J,LESKOVEC J.From amateurs to connoisseurs:modeling the evolution of user expertise through online reviews
[C]∥Proceedings of the 22nd International Conference on World Wide Web.Rio de Janeiro,Brazil:Association for Computing Machinery,2013:897-908.
[22]KRAUS M,FEUERRIEGEL S.Sentiment analysis based on rhetorical structure theory:Learning deep neural networks from discourse trees[J].Expert Systems with Applications,2019,118:65-79.
[23]JOSHI A,JAIN P,BHATTACHARYYA P,et al.Who would have thought of that!:A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection[C]∥Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM).2016:1-10.
[24]WANG X,JIANG W,LUO Z.Combination of convolutional and recurrent neural network for sentiment analysis of short texts[C]∥Proceedings of the 26th International Conference on Computational Linguistics.Osaka,Japan:Association for Computational Linguistics,2016:2428-2437.
[25]KIM Y.Convolutional neural networks for sentence classification[C]∥ Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).Doha,Qatar:Association for Computational Linguistics,2014:1746-1751.
[1] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[2] ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63.
[3] DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145.
[4] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[5] XIONG Li-qin, CAO Lei, LAI Jun, CHEN Xi-liang. Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization [J]. Computer Science, 2022, 49(9): 172-182.
[6] WANG Ming, PENG Jian, HUANG Fei-hu. Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction [J]. Computer Science, 2022, 49(8): 40-48.
[7] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[8] QIN Qi-qi, ZHANG Yue-qin, WANG Run-ze, ZHANG Ze-hua. Hierarchical Granulation Recommendation Method Based on Knowledge Graph [J]. Computer Science, 2022, 49(8): 64-69.
[9] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[10] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[11] YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236.
[12] ZHANG Yuan, KANG Le, GONG Zhao-hui, ZHANG Zhi-hong. Related Transaction Behavior Detection in Futures Market Based on Bi-LSTM [J]. Computer Science, 2022, 49(7): 31-39.
[13] ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105.
[14] ZENG Zhi-xian, CAO Jian-jun, WENG Nian-feng, JIANG Guo-quan, XU Bin. Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism [J]. Computer Science, 2022, 49(7): 106-112.
[15] XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!