Presenting a Framework for Intelligent Sentiment Analysis Using a Novel Method of feature Combination and Meta-Initiative in Particle Swarm Optimization

Document Type : Original Article

Authors

1 Assistant Professor of the Department of Knowledge Management, Faculty of Social Sciences, Command University and Aja.

2 Assistant Professor of Mathematics Department, Faculty of Basic, Electronic Campus of Azad University.

10.48301/kssa.2023.386361.2458

Abstract

Today, with the increase in the use of the internet, people have turned to the internet to buy their products or to learn about various topics. There are a large number of virtual pages where users post their opinions on various topics. A large amount of data exists which extracting useful information from is a costly and time-consuming task. Opinion mining is the process of intelligent analysis of the sentiments of users who have expressed their opinions in relation to a specific topic with the capability of extract them. The machine learning method is one of the most optimal and efficient methods for extracting knowledge from users' opinions on the offered products. In these methods, the training data of a system is given to classify user opinions. One of the most important classification steps is data reduction. By using the new feature combination method, the set of extracted features can be reduced to a greater extent than the feature selection method, which leads to a subset of useful information with a much smaller volume and higher recognition power. In this research, particle group optimization algorithm was used to optimize the combination of features. To evaluate the proposed method, MATLAB software was used to evaluate the proposed method, and experiments were conducted on four data sets. The results of the research showed that the use of the feature combination method increased the efficiency of classification and reduced the effect of this increase in the decrease of the efficiency of the classifier.

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References

[1] Hosseinian, A. H., Teimourpour, B., & Jamali Hondori, B. (2019). A Hybrid Algorithm for Detecting Communities of Social Networks based on the Modularity Density Criterion. Business Intelligence Management Studies, 8(29), 61-86. https://doi.org/10.22054/i ms.2019.10376
[2] Mohammadi, S., & Nazemi, E. (2021). Sentiment Analysis at the Product Feature Level and Based on Users Gender. Business Intelligence Management Studies, 10(37), 267-296. https://doi.org/10.22054/ims.2021.52110.1723
[3] Emary, E., Zawbaa, H. M., Grosan, C., & Hassenian, A. E. (2015). Feature Subset Selection Approach by Gray-Wolf Optimization. In A. Abraham, P. Krömer, & V. Snasel (Eds.), Afro-European Conference for Industrial Advancement. Springer International Publishing. https://doi.org/10.1007/978-3-319-13572-4_1
[4] Athar, A., Butt, W. H., Anwar, M. W., Latif, M., & Azam, F. (2017, February 24-26). Exploring the Ensemble of Classifiers for Sentimental Analysis: A Systematic Literature Review. Proceedings of the 9th International Conference on Machine Learning and Computing, Singapore, Asia. https://doi.org/10.1145/3055635.3056601
[5] Khadem, M., Toloie Eshlaghy, A., & Fathi Hafshejani, K. (2023). Introducing a new meta-heuristic algorithm to solve the feature selection problem. Journal of Future Studies Management, 33(3), 16-27. https://doi.org/10.30495/jmfr.2022.55572.2264
[6] Abualigah, L. M. Q. (2019). Feature selection and enhanced krill herd algorithm for text document clustering. Springer Cham. https://doi.org/10.1007/978-3-030-10674-4
[7] Mafarja, M., & Mirjalili, S. (2018). Whale optimization approaches for wrapper feature selection. Applied Soft Computing, 62, 441-453. https://doi.org/10.1016/j.asoc.2017 .11.006
[8] Ghaemi, M., & Feizi-Derakhshi, M-R. (2016). Feature selection using Forest Optimization Algorithm. Pattern Recognition, 60, 121-129. https://doi.org/10.1016/j.patcog.2016 .05.012
[9] Kashef, S., & Nezamabadi-pour, H. (2015). An advanced ACO algorithm for feature subset selection. Neurocomputing, 147, 271-279. https://doi.org/10.1016/j.neucom.2014.0 6.067
[10] Hosseini, M., & Navabi, M. S. (2023). Hybrid PSO-GSA based approach for feature selection. Journal of Industrial Engineering and Management Studies, 10(1), 1-15. https://jie ms.icms.ac.ir/article_166460.html
[11] Mohammadi, S., & Khalaj, E. (2021). Presenting the model for opinion mining at the document feature level for hotel users' reviews. Journal of Information and Communication Technology, 13(49-50), 85-102. http://rimag.ricest.ac.ir/fa/Article/16379
[12] Ibrahim, N. F., & Wang, X. (2019). Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media. Computers in Human Behavior, 96, 32-45. https://doi.org/10.1016/j.chb.2019.02.004
[13] Nazim Sha, S., & Rajeswari, M. (2019, February 26-28). Creating a Brand Value and Consumer Satisfaction in E-Commerce Business Using Artificial Intelligence. Proceedings of International Conference on Sustainable Computing in Science, Technology and Management  Amity University Rajasthan, Jaipur-India. https://papers.ssrn.com/sol 3/papers.cfm?abstract_id=3351618
[14] Khan, F. H., Qamar, U., & Bashir, S. (2016). SentiMI: Introducing point-wise mutual information with SentiWordNet to improve sentiment polarity detection. Applied Soft Computing, 39, 140-153. https://doi.org/10.1016/j.asoc.2015.11.016
[15] Dehghani Ashkazari, S., Derhami, V., Zare Bidoki, A. M., & Basiri, M. E. (2020). Persian Opinion Mining based on Transfer Learning. Tabriz Journal Of Electrical Engineering, 50(3), 1215-1224. https://tjee.tabrizu.ac.ir/article_11360.html?lang=en
[16] Yousefpour, A., Ibrahim, R., & Hamed, H. N. A. (2017). Ordinal-based and frequency-based integration of feature selection methods for sentiment analysis. Expert Systems with Applications, 75, 80-93. https://doi.org/10.1016/j.eswa.2017.01.009
Karafan Quarterly Scientific Journal
Volume 20, Issue 3
Engineering
December 2024
Pages 531-551

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History

  • Receive Date: 07 March 2023
  • Revise Date: 24 July 2023
  • Accept Date: 30 August 2023

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