Một phương pháp dữ liệu lớn cho phân tích cảm xúc sử dụng chọn lọc đặc trưng tham lam với mạng nơ-ron bộ nhớ ngắn hạn và dài hạn dựa trên tối ưu hóa bầy mèo

Zhang L, Liu B (2017) Sentiment analysis and opinion mining. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning and data mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_907 Lee G, un Jeong J, Seo S, Kim C (2018) Sentiment classification with word localization based on weakly supervised learning with a convolutional neural network. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2018.04.006 Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2:1–135 Bhatia S, Sharma M, Bhatia KK (2018) Sentiment analysis and mining of opinions. Internet of things and big data analytics toward next-generation intelligence. Springer, Cham, pp 503–523 Tolba A, Elashkar E (2018) Soft computing approaches based bookmark selection and clustering techniques for social tagging systems. Cluster Comput 1–7. https://doi.org/10.1007/s10586-018-2014-5 Liu Y, Gao C, Zhang Z, Lu Y, Chen S, Liang M, Tao L (2017) Solving NP-hard problems with Physarum-based ant colony system. IEEE/ACM Trans Comput Biol Bioinf 14:108–120 Nabaei A, Hamian M, Parsaei MR, Safdari R, Samad-Soltani T, Zarrabi H, Ghassemi A (2018) Topologies and performance of intelligent algorithms: a comprehensive review. Artif Intell Rev 49:79–103 Roy S, Biswas S, Chaudhuri SS (2014) Nature-inspired swarm intelligence and its applications. Int J Mod Educ Comp Sci 12:55–65 Mahi M, Baykan OK, Kodaz H (2018) A new approach based on particle swarm optimization algorithm for solving data allocation problem. Appl Soft Comput 62:571–578 Pandey HM, Rajput M, Mishra V (2018) Performance comparison of pattern search, simulated annealing, genetic algorithm and jaya algorithm. Data engineering and intelligent computing. Springer, Singapore, pp 377–384 Gill SS, Buyya R, Chana I, Singh M, Abraham A (2018) BULLET: particle swarm optimization based scheduling technique for provisioned cloud resources. J Netw Sys Manag 26:361–400 Bhalla R, Jain P (2016) A model based on effective and intelligent sentiment mining: a review. Indian J Sci Technol 9:32 Nikitidis S, Nikolaidis N, Pitas I (2012) Multiplicative update rules for incremental training of multiclass support vector machines. Pattern Recognit 45:1838–1852 Kingma DP, Mohamed S, Rezende DJ, Welling M (2014) Semi-supervised learning with deep generative models. Adv Neural Inf Proc Sys 2:3581–3589 Isaac T, García S, Herrera F (2015) Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study. Knowl Inf Sys 42:245–284 Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the International Conference on Machine Learning, pp 282–289 Astorino A, Fuduli A (2015) Support vector machine polyhedral separability in semi supervised learning. J Optim Theory Appl 164:1039–1050 Zhang Z, Zhao M, Chow TWS (2015) Graph based constrained semi-supervised learning framework via label propagation over adaptive neighborhood. IEEE Trans Knowl Data Eng 27:2362–2376 Subramanya A, Bilmes J (2011) Semi-supervised learning with measure propagation. J Mach Learn Res 12:3311–3370 Cecotti H (2016) Active graph based semi-supervised learning using image matching: application to handwritten digit recognition. Pattern Recognit Lett. 73:76–82 Patel H, Thakur GS (2016) A hybrid weighted nearest neighbor approach to mine imbalanced data. In: Proceeding 12th International Conference Data Mining (ICDM). IEEE, Las Vegas, pp 106–111 Lu J, Behbood V, Hao P, Zuo H, Xue S, Zhang G (2015) Transfer learning using computational intelligence: a survey. Knowl-Based Sys 80:14–23 Perlich C, Dalessandro B, Raeder T, Stitelman O, Provost F (2015) Machine learning for targeted display advertising: transfer learning in action. Mach Learn 95:103–127 Long M, Wang J, Ding G, Pan SJ, Yu PS (2014) Adaptation regularization: a general framework for transfer learning. IEEE Trans Knowl Data Eng 26:1076–1089 Wang B, Pineau J (2016) Online boosting algorithms for anytime transfer and multitask learning. In: Proceedings 29th AAAI Conference Artificial Intelligence, AAAI, Austin, pp 3038–3044 Kumar A, Khorwal R (2017) Firefly algorithm for feature selection in sentiment analysis. Computational intelligence in data mining. Springer, Singapore, pp 693–703 Nayak J, Naik B, Behera HS (2016) A novel nature inspired firefly algorithm with higher order neural network: performance analysis. Eng Sci Technol 19:197–211 Chakraborty B, Kawamura A (2018) A new penalty-based wrapper fitness function for feature subset selection with evolutionary algorithms. J Inf Telecommun 2:1–18. https://doi.org/10.1080/24751839.2018.1423792 La L, Cao S, Qin L (2018) Take full advantage of unlabeled data for sentiment classification. Kybernetes 47:474–486 Black PE (2005) Greedy algorithm. Dictionary of Algorithms and Data Structures. U.S, National Institute of Standards and Technology (NIST), Gaithersburg Hazewinkel M (ed) (2001) [1994] Greedy algorithm. Encyclopedia of mathematics. Springer/Kluwer Academic Publishers, Dordrecht. ISBN 978-1-55608-010-4 Gers FA, Schmidhuber E (2001) LSTM recurrent networks learn simple context-free and context-sensitive languages. IEEE Trans Neural Netw 12:1333–1340. https://doi.org/10.1109/72.963769.ISSN1045-9227 Yang X-S, Sadat Hosseini SS, Gandomi AH (2012) Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 12:1180–1186 Kumar A, Mishra D (2013) Cat swarm based optimization of gene expression data classification. Int J Comp Trends Technol (IJCTT) 4:1185 Meysam O, Yasin O, Mohammad M, Mohammad T (2013) A novel cat swarm optimization algorithm for unconstrained optimization problems. Int J Inf Technol Comp Sci 11:32–41