An Optimization-based Learning Black Widow Optimization Algorithm for Text Psychology
الموضوعات :
Ali Hosseinalipour
1
(
Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, IRAN
)
Farhad Soleimanian Gharehchopogh
2
(
Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, IRAN
)
mohammad masdari
3
(
Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.
)
ALi Khademi
4
(
Department of Psychology Science, Urmia Branch, Islamic Azad University, Urmia, IRAN.
)
الکلمات المفتاحية: black widow optimization algorithm, Meta-Heuristic Algorithm, text psychology, Feature Selection,
ملخص المقالة :
In recent years, social networks' growth has led to an increase in these networks' content. Therefore, text mining methods became important. As part of text mining, Sentiment analysis means finding the author's perspective on a particular topic. Social networks allow users to express their opinions and use others' opinions in other people's opinions to make decisions. Since the comments are in the form of text and reading them is time-consuming. Therefore, it is essential to provide methods that can provide us with this knowledge usefully. Black Widow Optimization (BWO) is inspired by black widow spiders' unique mating behavior. This method involves an exclusive stage, namely, cannibalism. For this reason, at this stage, species with an inappropriate evaluation function are removed from the circle, thus leading to premature convergence. In this paper, we first introduced the BWO algorithm into a binary algorithm to solving discrete problems. Then, to reach the optimal answer quickly, we base its inputs on the opposition. Finally, to use the algorithm in the property selection problem, which is a multi-objective problem, we convert the algorithm into a multi-objective algorithm. The 23 well-known functions were evaluated to evaluate the performance of the proposed method, and good results were obtained. Also, in evaluating the practical example, the proposed method was applied to several emotion datasets, and the results indicate that the proposed method works very well in the psychology of texts.
1. Gharehchopogh, F.S. and H. Gholizadeh, A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm and Evolutionary Computation, 2019. 48: p. 1-24.
2. Gharehchopogh, F.S., I. Maleki, and Z.A. Dizaji, Chaotic vortex search algorithm: metaheuristic algorithm for feature selection. Evolutionary Intelligence, 2021: p. 1-32.
3. Abdollahzadeh, B. and F.S. Gharehchopogh, A multi-objective optimization algorithm for feature selection problems. Engineering with Computers, 2021: p. 1-19.
4. Jafari, N. and F. Soleimanian Gharehchopogh, An Improved Bat Algorithm with Grey Wolf Optimizer for Solving Continuous Optimization Problems. Journal of Advances in Computer Engineering and Technology, 2020. 6(3): p. 119-130.
5. Mohmmadzadeh, H. and F.S. Gharehchopogh, An efficient binary chaotic symbiotic organisms search algorithm approaches for feature selection problems. The Journal of Supercomputing, 2021: p. 1-43.
6. Rahnema, N. and F.S. Gharehchopogh, An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering. Multimedia Tools and Applications, 2020. 79(43): p. 32169-32194.
7. Hosseinalipour, A., et al., A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology. Applied Intelligence: p. 1-36.
8. Sayed, S.A.-F., E. Nabil, and A. Badr, A binary clonal flower pollination algorithm for feature selection. Pattern Recognition Letters, 2016. 77: p. 21-27.
9. Gharehchopogh, F.S., H. Shayanfar, and H. Gholizadeh, A comprehensive survey on symbiotic organisms search algorithms. Artificial Intelligence Review, 2019: p. 1-48.
10. Zorarpacı, E. and S.A. Özel, A hybrid approach of differential evolution and artificial bee colony for feature selection. Expert Systems with Applications, 2016. 62: p. 91-103.
11. Hosseinalipour, A., et al., Toward text psychology analysis using social spider optimization algorithm. Concurrency and Computation: Practice and Experience. n/a(n/a): p. e6325.
12. Dong, H., et al., A novel hybrid genetic algorithm with granular information for feature selection and optimization. Applied Soft Computing, 2018. 65: p. 33-46.
13. Liu, B. and L. Zhang, A survey of opinion mining and sentiment analysis, in Mining text data. 2012, Springer. p. 415-463.
14. Nasukawa, T. and J. Yi. Sentiment analysis: Capturing favorability using natural language processing. in Proceedings of the 2nd international conference on Knowledge capture. 2003.
15. Asghar, M.Z., et al., A review of feature extraction in sentiment analysis. Journal of Basic and Applied Scientific Research, 2014. 4(3): p. 181-186.
16. Saeys, Y., I. Inza, and P. Larrañaga, A review of feature selection techniques in bioinformatics. bioinformatics, 2007. 23(19): p. 2507-2517.
17. Sharma, M. and P. Kaur, A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem. Archives of Computational Methods in Engineering, 2020: p. 1-25.
18. Emine, B. and E. Ülker, An efficient binary social spider algorithm for feature selection problem. Expert Systems with Applications, 2020. 146: p. 113185.
19. Hayyolalam, V. and A.A.P. Kazem, BWO algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 2020. 87: p. 103249.
20. Pang, B., L. Lee, and S. Vaithyanathan, Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070, 2002.
21. Arora, S. and P. Anand, Binary butterfly optimization approaches for feature selection. Expert Systems with Applications, 2019. 116: p. 147-160.
22. Hussien, A.G., et al., S-shaped binary whale optimization algorithm for feature selection, in Recent trends in signal and image processing. 2019, Springer. p. 79-87.
23. Bennasar, M., Y. Hicks, and R. Setchi, Feature selection using joint mutual information maximisation. Expert Systems with Applications, 2015. 42(22): p. 8520-8532.
24. Mirjalili, S., Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 2016. 27(4): p. 1053-1073.
25. Yang, X.S. and A.H. Gandomi, Bat algorithm: a novel approach for global engineering optimization. Engineering computations, 2012.
26. Leskovec, J., A. Rajaraman, and J.D. Ullman, Mining of massive data sets. 2020: Cambridge university press.
27. Mafarja, M.M. and S. Mirjalili, Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing, 2017. 260: p. 302-312.
28. Liao, T.W. and R. Kuo, Five discrete symbiotic organisms search algorithms for simultaneous optimization of feature subset and neighborhood size of knn classification models. Applied Soft Computing, 2018. 64: p. 581-595.
29. Mafarja, M., et al., Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowledge-Based Systems, 2018. 145: p. 25-45.
30. Rajamohana, S. and K. Umamaheswari, Hybrid approach of improved binary particle swarm optimization and shuffled frog leaping for feature selection. Computers & Electrical Engineering, 2018. 67: p. 497-508.
31. Azar, A.T., et al., A random forest classifier for lymph diseases. Computer methods and programs in biomedicine, 2014. 113(2): p. 465-473.
