Sequential Three-Way Sentiment Analysis Based on Temporal-Spatial Multi-granularity
YANG Xin1, LIU Dun2, LI Qiuke1, YANG Xibei3
1. School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130 2. School of Economics and Management, Southwest Jiaotong University, Chengdu 610031 3. School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003
Abstract：The traditional static methods of sentiment analysis cannot meet the quantity and complexity requirements of dynamic data in the big data era. Therefore, grounded on the concept of sequential three-way decisions, a sequential three-way sentiment analysis framework based on temporal-spatial multi-granularity is proposed to overcome the shortcomings of the traditional two-way decisions. Firstly, a multi-layer granular structure with temporal-spatial features is constructed using increasing data and better-fitting feature space over time to balance the misclassification cost and training cost. Then, three typical sentiment classification methods are applied as benchmarks to test the efficiency of the proposed method. Finally, compared with the static methods, experimental results on two datasets show that the proposed method greatly reduces the classification costs with the classification quality maintained.
杨新, 刘盾, 李楸柯, 杨习贝. 基于时空多粒度的序贯三支情感分析[J]. 模式识别与人工智能, 2020, 33(8): 743-752.
YANG Xin, LIU Dun, LI Qiuke, YANG Xibei. Sequential Three-Way Sentiment Analysis Based on Temporal-Spatial Multi-granularity. , 2020, 33(8): 743-752.
[1] LIU B. Sentiment Analysis and Opinion Mining // LIU B, ed. Synthesis Lectures on Human Language Technologies. San Rafael, USA: Morgan & Claypol Publishers, 2012: 1-167. [2] STEVENSON R A, MIKELS J A, JAMES T W. Characterization of the Affective Norms for English Words by Discrete Emotional Categories. Behavior Research Methods, 2007, 39(4): 1020-1024. [3] FANG X, ZHAN J. Sentiment Analysis Using Product Review Data. Journal of Big Data, 2015, 2(1). DOI: 10.1186/s40537-015-0015-2. [4] PANG B, LEE L, VAITHYANATHAN S. Thumbs Up: Sentiment Classification Using Machine Learning Techniques // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2002: 79-86. [5] GO A, BHAYANI R, HUANG L. Twitter Sentiment Classification Using Distant Supervision[C/OL]. [2020-06-01]. https://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf. [6] SUI H Y, KHOO C, CHAN S. Sentiment Classification of Product Reviews Using SVM and Decision Tree Induction. Advances in Classification Research Online, 2003, 14(1): 42-52. [7] LIANG H, SUN X, SUN Y L, et al. Text Feature Extraction Based on Deep Learning: A Review. EURASIP Journal on Wireless Communications and Networking, 2017. DOI: 10.1186/s13638-017-0993-1. [8] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient Estimation of Word Representations in Vector Space[C/OL]. [2020-06-01]. https://arxiv.org/pdf/1301.3781.pdf. [9] KIM Y. Convolutional Neural Networks for Sentence Classification // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2014: 1746-1751. [10] HUANG Z H, XU W, YU K. Bidirectional LSTM-CRF Models for Sequence Tagging[C/OL]. [2020-06-01]. https://arxiv.org/pdf/1508.01991.pdf. [11] GLOROT X, BORDES A, BENGIO Y. Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach // Proc of the 28th International Conference on Machine Learning. New York, USA: ACM, 2011: 513-520. [12] 陈 珂,梁 斌,柯文德,等.基于多通道卷积神经网络的中文微博情感分析.计算机研究与发展, 2018, 55(5): 945-957. (CHEN K, LIANG B, KE W D, et al. Chinese Micro-Blog Sentiment Analysis Based on Multi-channels Convolutional Neural Networks. Journal of Computer Research and Development, 2018, 55(5): 945-957.) [13] ZHANG L, WANG S, LIU B. Deep Learning for Sentiment Analysis: A Survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018, 8(4). DOI: 10.1002/widm.1253. [14] YAO Y Y. Three-Way Decisions with Probabilistic Rough Sets. Information Sciences, 2010, 180(3): 341-353. [15] YAO Y Y. Three-Way Decision and Granular Computing. International Journal of Approximate Reasoning, 2018, 103: 107-123. [16] YAO Y Y. Three-Way Decisions and Cognitive Computing. Cognitive Computation, 2016, 8(4): 543-554. [17] ZHOU B, YAO Y Y, LUO J G. Cost-Sensitive Three-Way Email Spam Filtering. Journal of Intelligent Information Systems, 2014, 42(1): 19-45. [18] REN F J, WANG L. Sentiment Analysis of Text Based on Three-Way Decisions. Journal of Intelligent and Fuzzy Systems, 2017, 33(1): 245-254. [19] LI H X, ZHANG L A, HUANG B, et al. Sequential Three-Way Decision and Granulation for Cost-Sensitive Face Recognition. Knowledge-Based Systems, 2016, 91: 241-251. [20] YAO Y Y. An Outline of a Theory of Three-Way Decisions // Proc of the International Conference on Rough Sets and Current Trends in Computing. Berlin, Germany: Springer, 2012: 1-17. [21] YAO Y Y, DENG X F. Sequential Three-Way Decisions with Pro-babilistic Rough Sets // Proc of the 10th IEEE International Conference on Cognitive Informatics and Cognitive Computing. Washington, USA: IEEE, 2011: 120-125. [22] YANG X, LI T K, LIU D, et al. A Unified Framework of Dynamic Three-Way Probabilistic Rough Sets. Information Sciences, 2017, 420: 126-147. [23] YAO Y Y. Granular Computing and Sequential Three-Way Decisions // Proc of the 8th International Conference on Rough Sets and Knowledge Technology. Berlin, Germany: Springer, 2013: 16-27. [24] YANG X, LI T K, FUJITA H, et al. A Unified Model of Sequential Three-Way Decisions and Multilevel Incremental Processing. Knowledge-Based Systems, 2017, 134: 172-188. [25] LI H X, ZHOU X Z, HUANG B, et al. Cost-Sensitive Three-Way Decision: A Sequential Strategy // Proc of the 8th International Conference on Rough Sets and Knowledge Technology. Berlin, Germany: Springer, 2013: 325-337. [26] YANG X, LI T R, FUJITA H, et al. A Sequential Three-Way Approach to Multi-class Decision. International Journal of Approximate Reasoning, 2019, 104: 108-125. [27] YANG X, LI T R, LIU D, et al. A Temporal-Spatial Composite Sequential Approach of Three-Way Granular Computing. Information Sciences, 2019, 486: 171-189. [28] ZHANG Y B, ZHANG Z F, MIAO D Q, et al. Three-Way Enhanced Convolutional Neural Networks for Sentence-Level Sentiment Classification. Information Sciences, 2019, 477: 55-64. [29] ZHANG Y B, MIAO D Q, WANG J Q, et al. A Cost-Sensitive Three-Way Combination Technique for Ensemble Learning in Sentiment Classification. International Journal of Approximate Reasoning, 2019, 105: 85-97. [30] LIU P F, QIU X P, HUANG X J. Recurrent Neural Network for Text Classification with Multi-task Learning // Proc of the 25th International Joint Conference on Artificial Intelligence. New York, USA: ACM, 2016: 2873-2879. [31] LAI S W, XU L H, LIU K, et al. Recurrent Convolutional Neural Networks for Text Classification // Proc of the 29th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2015: 2267-2273. [32] MAAS A L, DALY R E, PHAM P T, et al. Learning Word Vectors for Sentiment Analysis // Proc of the 49th Annual Meeting of the Association for Computational Linguistics(Human Language Technologies). Stroudsburg, USA: ACL, 2011: 142-150. [33] ZHANG X, ZHAO J B, LECUN Y. Character-Level Convolutional Networks for Text Classification[C/OL]. [2020-06-01]. https://arxiv.org/pdf/1509.01626.pdf. [34] WU C L, CHAU K W, LI Y S. Methods to Improve Neural Network Performance in Daily Flows Prediction. Journal of Hydrology, 2009, 372(1/2/3/4): 80-93.
2020, Vol. 33 
