Computer Science ›› 2019, Vol. 46 ›› Issue (7): 151-156.doi: 10.11896/j.issn.1002-137X.2019.07.024

• Artificial Intelligence • Previous Articles     Next Articles

Sentiment Classification Towards Question-Answering Based on Bidirectional Attention Mechanism

SHEN Chen-lin,ZHANG Lu,WU Liang-qing,LI Shou-shan   

  1. (School of Computer Science & Technology,Soochow University,Suzhou,Jiangsu 215006,China)
  • Received:2018-06-12 Online:2019-07-15 Published:2019-07-15

Abstract: Sentiment classification is a fundamental task in natural language processing,which aims at inferring the sentiment polarity of a given text.Previous studies for sentiment classification,mainly focus on sentence,document and tweet text styles.Different from these researches,this paper focused on a novel text style,i.e.,question-answering (QA) review,for sentiment classification.Firstly,a large-scale and high-quality QA review corpus was collected and built.Then,a bidirectional attention neural network for QA sentiment classification was proposed.Specifically,the question and answer text with Bi-LSTM were encoded respectively.After that,sentiment weights in question and answer text were calculated synchronously by employing bidirectional attention mechanism.Finally,the sentiment matching representation for each QA review with sentiment weights can be obtained.Empirical studies show that the proposed approach achieves a great result (75.5% in Accuracy and 61.4% in Macro F1),and has remarkable improvement compared with other baselines.

Key words: Sentiment classification, Attention mechanism, Question-Answering

CLC Number: 

  • TP391
[1] ZHAO Y Y,QIN B,LIU T.Sentiment Analysis [J].Journal of Software,2010,21(8):1834-1848.(in Chinese)赵妍妍,秦兵,刘挺.文本情感分析 [J].软件学报,2010,21(8):1834-1848.
[2] ZHANG Y F,LAI G K,ZHANG M,et al.Explicit Factor Mo- dels for Explainable Recommendation Based on phrase-level Sentiment Analysis[C]∥Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval.2014:83-92.
[3] CHAMBERS N,BOWEN V,GENCO E,et al.Identifying Politi- cal Sentiment between Nation States with Social Media[C]∥Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:65-75.
[4] WANG R H,CUI X M,ZHOU W,et al.Research of Text Sen- timent Classification Based on Improved Semantic Comprehension [J].Computer Science,2017,44(S2):92-97.(in Chinese)王日宏,崔兴梅,周炜,等.改进的基于语义理解的文本情感分类方法研究 [J].计算机科学,2017,44(S2):92-97.
[5] XU J C,CHEN D L,QIU X P,et al.Cached Long Short-Term Memory Neural Networks for Document-level Sentiment Classification[C]∥Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016:1660-1669.
[6] TURKEY P D.Thumbs up or Thumbs down?:Semantic Orientation Applied to Unsupervised Classification of Reviews[C]∥Proceedings of the 40th Annual Meeting on Association for Computational Linguistics.2002:417-424.
[7] TANG D Y,WEI F R,YANG N,et al.Learning Sentiment Specific Word Embedding for Twitter Sentiment Classification[C]∥Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.2014:1555-1565.
[8] MATSUMOTO S,TAKAMURA H,OKUMURA M.Senti- ment Classification Using Word Sub-sequence and Dependency Sub-trees[C]∥Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining.2005:301-311.
[9] ZHOU X J,WAN X J,XIAO J G.Cross-lingual Sentiment Classification with Bilingual Document Representation Learning[C]∥Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.2016:1403-1412.
[10] 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.2010:414-423.
[11] XIA R,WANG C,DAI X Y,et al.Co-training for Semi-supervised Sentiment Classification Based on Dual-view Bags-of-words Representation[C]∥Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing.2015:1054-1063.
[12] PONTIKI M,GALANIS D,PAVLOPOULOS J,et al.SemEval-2014 Task 4:Aspect Based Sentiment Analysis[C]∥Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016).2016:19-30.
[13] WANG Y Q,HUANG M L,ZHAO L,et al.Attention-based LSTM for Aspect-level Sentiment Classification[C]∥Procee-dings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016:606-615.
[14] TANG D Y,QIN B,LIU T.Aspect Level Sentiment Classification with Deep Memory Network[C]∥Proceedings of the 2016 Conference on Empirical Methods in Natural Language Proces-sing.2016:214-224.
[15] HOCHREITER S,SCHMIDHUBER J.Long Short-term Me- mory [J].Neural Computation,1997,9(8):1735-1780.
[16] LUONG T,PHAM H,MANNINGC D.Effective Approaches to Attention-based Neural Machine Translation[C]∥Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:1412-1421.
[17] PENG Y,HE X,ZHAO J.Object-part Attention Model for Fine-grained Image Classification [J].IEEE Transactions on Image Processing,2018,27(3):1487-1500.
[18] YANG Z C,YANG D Y,DYER C,et al.Hierarchical Attention Networks for Document Classification[C]∥Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2016:1480-1489.
[19] CUI Y M,CHEN Z P,WEI S,et al.Attention-over-attention Neural Networks for Reading Comprehension[C]∥Proceedings of the 55th Annual Meeting of the Association for Computatio-nal Linguistics.2017:593-602.
[20] KINGMA D,BA J.Adam:A Method for Stochastic Optimization [C]∥Proceedings of the 3rd International Conference on Learning Representations.2015:1-15.
[21] LEI T,BARZILAY R,JAAKKOLA T.Molding CNNs for Text:Non-linear,Non-consecutive Convolutions[C]∥Procee-dings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:1565-1575.
[22] WANG Z G,HAMZA W,FLORIAN R.Bilateral Multi-perspec- tive Matching for Natural Language Sentences[C]∥Proceedings of the 2017 International Joint Conferences on Artificial Intelligence.2017:4144-4150.
[1] ZHAO Jia-qi, WANG Han-zheng, ZHOU Yong, ZHANG Di, ZHOU Zi-yuan. Remote Sensing Image Description Generation Method Based on Attention and Multi-scale Feature Enhancement [J]. Computer Science, 2021, 48(1): 190-196.
[2] LIU Yang, JIN Zhong. Fine-grained Image Recognition Method Combining with Non-local and Multi-region Attention Mechanism [J]. Computer Science, 2021, 48(1): 197-203.
[3] WANG Rui-ping, JIA Zhen, LIU Chang, CHEN Ze-wei, LI Tian-rui. Deep Interest Factorization Machine Network Based on DeepFM [J]. Computer Science, 2021, 48(1): 226-232.
[4] WANG Run-zheng, GAO Jian, HUANG Shu-hua, TONG Xin. Malicious Code Family Detection Method Based on Knowledge Distillation [J]. Computer Science, 2021, 48(1): 280-286.
[5] PAN Zu-jiang, LIU Ning, ZHANG Wei, WANG Jian-yong. MTHAM:Multitask Disease Progression Modeling Based on Hierarchical Attention Mechanism [J]. Computer Science, 2020, 47(9): 185-189.
[6] ZHAO Wei, LIN Yu-ming, WANG Chao-qiang, CAI Guo-yong. Opinion Word-pairs Collaborative Extraction Based on Dependency Relation Analysis [J]. Computer Science, 2020, 47(8): 164-170.
[7] YUAN Ye, HE Xiao-ge, ZHU Ding-kun, WANG Fu-lee, XIE Hao-ran, WANG Jun, WEI Ming-qiang, GUO Yan-wen. Survey of Visual Image Saliency Detection [J]. Computer Science, 2020, 47(7): 84-91.
[8] LIU Yan, WEN Jing. Complex Scene Text Detection Based on Attention Mechanism [J]. Computer Science, 2020, 47(7): 135-140.
[9] YU Yi-lin, TIAN Hong-tao, GAO Jian-wei and WAN Huai-yu. Relation Extraction Method Combining Encyclopedia Knowledge and Sentence Semantic Features [J]. Computer Science, 2020, 47(6A): 40-44.
[10] NI Hai-qing, LIU Dan, SHI Meng-yu. Chinese Short Text Summarization Generation Model Based on Semantic-aware [J]. Computer Science, 2020, 47(6): 74-78.
[11] HUANG Yong-tao, YAN Hua. Scene Graph Generation Model Combining Attention Mechanism and Feature Fusion [J]. Computer Science, 2020, 47(6): 133-137.
[12] ZHANG Zhi-yang, ZHANG Feng-li, CHEN Xue-qin, WANG Rui-jin. Information Cascade Prediction Model Based on Hierarchical Attention [J]. Computer Science, 2020, 47(6): 201-209.
[13] DENG Yi-jiao, ZHANG Feng-li, CHEN Xue-qin, AI Qing, YU Su-zhe. Collaborative Attention Network Model for Cross-modal Retrieval [J]. Computer Science, 2020, 47(4): 54-59.
[14] ZHANG Peng-fei, LI Guan-yu, JIA Cai-yan. Truncated Gaussian Distance-based Self-attention Mechanism for Natural Language Inference [J]. Computer Science, 2020, 47(4): 178-183.
[15] YU Shan-shan, SU Jin-dian, LI Peng-fei. Sentiment Classification Method for Sentences via Self-attention [J]. Computer Science, 2020, 47(4): 204-210.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] 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 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] 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 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .