Document Type : Original Article
Authors
Department of Information Technology, Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Abstract
With the development and widespread use of social networks among people, high-volume data is produced and the analysis of this data can be useful in many areas, including people's daily lives. Classification of this volume of data using traditional methods is a very difficult, time-consuming, and low-accuracy task, therefore, using sentiment analysis techniques, people's opinions can be effectively summarized and categorized. To this end, we propose an algorithm that combines Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The reason for combining the two algorithms is that the GSA has a good ability to search overall, but in the last iterations, it has a low speed in exploiting the search space. Since the PSO algorithm has a special ability to exploit the search space, this algorithm is used in the exploitation phase to solve the problem. The accuracy obtained from our proposed algorithm (PSO-GSA) shows an improvement in the accuracy of the GSA algorithm.
Keywords
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Goel, L. and Garg, A. (2018). Sentiment Analysis of Social Networking Websites using Gravitational Search Optimization Algorithm. International Journal of Applied Evolutionary Computation (IJAEC), 9(1), 76-85. doi:10.4018/IJAEC.2018010105
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Karaboga, D. and Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471. doi:10.1007/s10898-007-9149-x
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Menghour, K. and Souici-Meslati, L. (2016). Hybrid ACO-PSO based approaches for feature selection. Int J Intell Eng Syst, 9(3), 65-79.
Mosavi, M. R., Khishe, M. and Moridi, A. (2021). Classification of Sonar Target using Hybrid Particle Swarm and Gravitational Search. Iranian journal of Marine technology, 3(1), 1-13.
Newton, I. (1729). In experimental philosophy particular propositions are inferred from the phenomena and afterwards rendered general by induction. Principia’, Book, 3.
Nguyen, H. B., Xue, B., Liu, I. and Zhang, M. (2014, 6-11 July 2014). Filter based backward elimination in wrapper based PSO for feature selection in classification. Paper presented at the 2014 IEEE Congress on Evolutionary Computation (CEC).
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . Dubourg, V. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
Ramchoun, H., Ghanou, Y., Ettaouil, M. and Janati Idrissi, M. A. (2016). Multilayer perceptron: Architecture optimization and training.
Rashedi, E., Nezamabadi-pour, H. and Saryazdi, S. (2009). GSA: A Gravitational Search Algorithm. Information Sciences, 179(13), 2232-2248. doi:https://doi.org/10.1016/j.ins.2009.03.004
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Sayce, D. (2020). The number of tweets per day in 2020. Retrieved on October.
Shojaee, Z., Shahzadeh Fazeli, S. A., Abbasi, E. and Adibnia, F. (2021). Feature Selection based on Particle Swarm Optimization and Mutual Information. Journal of AI and Data Mining, 9(1), 39-44. doi:10.22044/jadm.2020.8857.2020
Symeonidis, S., Effrosynidis, D. and Arampatzis, A. (2018). A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis. Expert Systems with Applications, 110, 298-310. doi:https://doi.org/10.1016/j.eswa.2018.06.022
Tawhid, M. A. and Dsouza, K. B. (2018). Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems. Applied Computing and Informatics.
Twitter dataset. (2014). Retrieved from https://drive.google.com/file/d/0BwPSGZHAP _ yoN2pZcVl1Qmp1OEU/view?usp=sharing
Yadav, A. and Vishwakarma, D. K. (2020). A comparative study on bio-inspired algorithms for sentiment analysis. Cluster Computing, 23(4), 2969-2989. doi:10.1007/s10586-020-03062-w
Yan, C., Ma, J., Luo, H. and Patel, A. (2019). Hybrid binary coral reefs optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical datasets. Chemometrics and Intelligent Laboratory Systems, 184, 102-111.
Yang, X.-S. (2009, 2009//). Firefly Algorithms for Multimodal Optimization. Paper presented at the Stochastic Algorithms: Foundations and Applications, Berlin, Heidelberg.
Yang, X. S. and Suash, D. (2009, 9-11 Dec. 2009). Cuckoo Search via Lévy flights. Paper presented at the 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).
Yousefpour, A., Ibrahim, R., Hamed, H. N. A. and Yokoi, T. (2016). Integrated feature selection methods using metaheuristic algorithms for sentiment analysis. Paper presented at the Asian Conference on Intelligent Information and Database Systems.
Yuvaraj, N. and Sabari, A. (2017). Twitter Sentiment Classification Using Binary Shuffled Frog Algorithm. Intelligent Automation & Soft Computing, 23(2), 373-381. doi:10.1080/10798587.2016.1231479
Zhou, Z., Zhang, D., Sun, Z. and Wang, J. (2015, 2015//). An Adaptive Hybrid PSO and GSA Algorithm for Association Rules Mining. Paper presented at the Cloud Computing and Security, Cham.
Ahuja, R., Chug, A., Kohli, S., Gupta, S. and Ahuja, P. (2019). The Impact of Features Extraction on the Sentiment Analysis. Procedia Computer Science, 152, 341-348. doi:https://doi.org/10.1016/j.procs.2019.05.008
Alarifi, A., Tolba, A., Al-Makhadmeh, Z. and Said, W. (2020). A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization-based long short-term memory neural networks. The Journal of Supercomputing, 76(6), 4414-4429.
Astuti, F. and Taufan, R. (2022). Sentiment Analysis of Covid-19 Vaccination on Twitter Using Classification Algorithms based on PSO. Sistemasi: Jurnal Sistem Informasi, 11(2), 364-376.
Basari, A. S. H., Hussin, B., Ananta, I. G. P. and Zeniarja, J. (2013). Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization. Procedia Engineering, 53, 453-462. doi:https://doi.org/10.1016/j.proeng.2013.02.059
Botchway, R. K., Yadav, V., Komínková, Z. O. and Senkerik, R. (2022). Text-based feature selection using binary particle swarm optimization for sentiment analysis. Paper presented at the 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET).
Chandra Pandey, A., Singh Rajpoot, D. and Saraswat, M. (2017). Twitter sentiment analysis using hybrid cuckoo search method. Information Processing & Management, 53(4), 764-779. doi:https://doi.org/10.1016/j.ipm.2017.02.004
Chen, H., Hou, Q., Han, L., Hu, Z., Ye, Z., Zeng, J. and Yuan, J. (2019, 18-21 Sept. 2019). Distributed Text Feature Selection Based On Bat Algorithm Optimization. Paper presented at the 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).
Goel, L. and Garg, A. (2018). Sentiment Analysis of Social Networking Websites using Gravitational Search Optimization Algorithm. International Journal of Applied Evolutionary Computation (IJAEC), 9(1), 76-85. doi:10.4018/IJAEC.2018010105
Gokalp, O., Tasci, E. and Ugur, A. (2020). A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification. Expert Systems with Applications, 146, 113176.
İ, İ., Atasoy, Ö. F. and Alçiçek, H. (2017, 5-8 Oct. 2017). Sentiment classification of social media data for telecommunication companies in Turkey. Paper presented at the 2017 International Conference on Computer Science and Engineering (UBMK).
Ighazran, H., Alaoui, L. and Boujiha, T. (2018, 21-23 Nov. 2018). Metaheuristic and Evolutionary Methods for Feature Selection in Sentiment Analysis (a Comparative Study). Paper presented at the 2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT).
Imandoust, S. B. and Bolandraftar, M. (2013). Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background. International journal of engineering research and applications, 3(5), 605-610.
Jianqiang, Z. and Xiaolin, G. (2017). Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis. IEEE Access, 5, 2870-2879. doi:10.1109/ACCESS.2017.2672677
Karaboga, D. and Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471. doi:10.1007/s10898-007-9149-x
Kennedy, J. and Eberhart, R. (1995, 27 Nov.-1 Dec. 1995). Particle swarm optimization. Paper presented at the Proceedings of ICNN'95 - International Conference on Neural Networks.
Kumar, A., Jaiswal, A., Garg, S., Verma, S. and Kumar, S. (2019). Sentiment Analysis Using Cuckoo Search for Optimized Feature Selection on Kaggle Tweets. International Journal of Information Retrieval Research (IJIRR), 9(1), 1-15. doi:10.4018/IJIRR.2019010101
Lai, C.-C., Wu, C.-H. and Tsai, M.-C. (2009). Feature selection using particle swarm optimization with application in spam filtering. International Journal of Innovative Computing, Information and Control, 5(2), 423-432.
Menghour, K. and Souici-Meslati, L. (2016). Hybrid ACO-PSO based approaches for feature selection. Int J Intell Eng Syst, 9(3), 65-79.
Mosavi, M. R., Khishe, M. and Moridi, A. (2021). Classification of Sonar Target using Hybrid Particle Swarm and Gravitational Search. Iranian journal of Marine technology, 3(1), 1-13.
Newton, I. (1729). In experimental philosophy particular propositions are inferred from the phenomena and afterwards rendered general by induction. Principia’, Book, 3.
Nguyen, H. B., Xue, B., Liu, I. and Zhang, M. (2014, 6-11 July 2014). Filter based backward elimination in wrapper based PSO for feature selection in classification. Paper presented at the 2014 IEEE Congress on Evolutionary Computation (CEC).
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . Dubourg, V. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
Ramchoun, H., Ghanou, Y., Ettaouil, M. and Janati Idrissi, M. A. (2016). Multilayer perceptron: Architecture optimization and training.
Rashedi, E., Nezamabadi-pour, H. and Saryazdi, S. (2009). GSA: A Gravitational Search Algorithm. Information Sciences, 179(13), 2232-2248. doi:https://doi.org/10.1016/j.ins.2009.03.004
Rathi, M., Malik, A., Varshney, D., Sharma, R. and Mendiratta, S. (2018, 2-4 Aug. 2018). Sentiment Analysis of Tweets Using Machine Learning Approach. Paper presented at the 2018 Eleventh International Conference on Contemporary Computing (IC3).
Sayce, D. (2020). The number of tweets per day in 2020. Retrieved on October.
Shojaee, Z., Shahzadeh Fazeli, S. A., Abbasi, E. and Adibnia, F. (2021). Feature Selection based on Particle Swarm Optimization and Mutual Information. Journal of AI and Data Mining, 9(1), 39-44. doi:10.22044/jadm.2020.8857.2020
Symeonidis, S., Effrosynidis, D. and Arampatzis, A. (2018). A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis. Expert Systems with Applications, 110, 298-310. doi:https://doi.org/10.1016/j.eswa.2018.06.022
Tawhid, M. A. and Dsouza, K. B. (2018). Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems. Applied Computing and Informatics.
Twitter dataset. (2014). Retrieved from https://drive.google.com/file/d/0BwPSGZHAP _ yoN2pZcVl1Qmp1OEU/view?usp=sharing
Yadav, A. and Vishwakarma, D. K. (2020). A comparative study on bio-inspired algorithms for sentiment analysis. Cluster Computing, 23(4), 2969-2989. doi:10.1007/s10586-020-03062-w
Yan, C., Ma, J., Luo, H. and Patel, A. (2019). Hybrid binary coral reefs optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical datasets. Chemometrics and Intelligent Laboratory Systems, 184, 102-111.
Yang, X.-S. (2009, 2009//). Firefly Algorithms for Multimodal Optimization. Paper presented at the Stochastic Algorithms: Foundations and Applications, Berlin, Heidelberg.
Yang, X. S. and Suash, D. (2009, 9-11 Dec. 2009). Cuckoo Search via Lévy flights. Paper presented at the 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).
Yousefpour, A., Ibrahim, R., Hamed, H. N. A. and Yokoi, T. (2016). Integrated feature selection methods using metaheuristic algorithms for sentiment analysis. Paper presented at the Asian Conference on Intelligent Information and Database Systems.
Yuvaraj, N. and Sabari, A. (2017). Twitter Sentiment Classification Using Binary Shuffled Frog Algorithm. Intelligent Automation & Soft Computing, 23(2), 373-381. doi:10.1080/10798587.2016.1231479
Zhou, Z., Zhang, D., Sun, Z. and Wang, J. (2015, 2015//). An Adaptive Hybrid PSO and GSA Algorithm for Association Rules Mining. Paper presented at the Cloud Computing and Security, Cham.
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