1
Mechatronics Department, Faculty of Engineering, Horus University in Egypt (HUE), Egypt
(mkhassan@horus.edu.eg)
2
Accounting Department, Faculty of Commerce, Kafr El Sheikh University, Egypt
(dina.abdelsalam@com.kfs.edu.eg)
3
Accounting Department, Faculty of Commerce, Mansoura University, Egypt
(metawee68@mans.edu.eg)
4
Dassault Systemes Deutschland GmbH, Meitnerstraße 8, 70563 Stuttgart, Germany
(bassem.hassan@3ds.com)
Abstract :
Sentimental Analysis (SA) becomes a familiar topic among business people, which is commonly applied for the classification of sentiments from online reviews. It is generally treated as a sentiment classification (SC) problem where the online reviews are categorized into positive or negative polarities using the words that exist in the online reviews. With this motivation, this paper presents a new K-means clustering with hybrid metaheuristic algorithm (KMC-HMA) for SA and classification. The proposed KMC-HMA technique initially performs data preprocessing to remove the unwanted words from the product reviews. In addition, K-means clustering technique is used for the clustering of the massive quantity of the applied product reviews. Moreover, the clustered data are fed into the classification model based on hybrid ant colony optimization (ACO) with dragonfly algorithm (DFA). The ACO algorithm is used for the classification of product reviews and the performance of the ACO algorithm can be optimally tuned by the use of DFA. The performance validation of the KMC-HMA technique is validated using two datasets such as Canon and ipod. The experimental values pointed out the superior performance of the KMC-HMA technique over the recent state of art techniques.
Keywords :
Sentiment analysis , Data classification , Metaheuristics , Clustering algorithm , Hybrid algorithms , Rule based classifier
References :
[1] Hasan, A., Moin, S., Karim, A. and Shamshirband, S., 2018. Machine learning-based sentiment analysis for twitter accounts. Mathematical and Computational Applications, 23(1), p.11.
[2] Baid, P., Gupta, A. and Chaplot, N., 2017. Sentiment analysis of movie reviews using machine learning techniques. International Journal of Computer Applications, 179(7), pp.45-49.
[3] Mitra, A., 2020. Sentiment Analysis Using Machine Learning Approaches (Lexicon based on movie review dataset). Journal of Ubiquitous Computing and Communication Technologies (UCCT), 2(03), pp.145-152.
[4] Jagdale, R.S., Shirsat, V.S. and Deshmukh, S.N., 2019. Sentiment analysis on product reviews using machine learning techniques. In Cognitive Informatics and Soft Computing (pp. 639-647). Springer, Singapore.
[5] Aziz, A.A. and Starkey, A., 2019. Predicting supervise machine learning performances for sentiment analysis using contextual-based approaches. IEEE Access, 8, pp.17722-17733.
[6] Rathi, M., Malik, A., Varshney, D., Sharma, R. and Mendiratta, S., 2018, August. Sentiment analysis of tweets using machine learning approach. In 2018 Eleventh international conference on contemporary computing (IC3) (pp. 1-3). IEEE.
[7] Valencia, F., Gómez-Espinosa, A. and Valdés-Aguirre, B., 2019. Price movement prediction of cryptocurrencies using sentiment analysis and machine learning. Entropy, 21(6), p.589.
[8] Abd El-Jawad, M.H., Hodhod, R. and Omar, Y.M., 2018, December. Sentiment analysis of social media networks using machine learning. In 2018 14th international computer engineering conference (ICENCO) (pp. 174-176). IEEE.
[9] Ahmad, M., Aftab, S., Muhammad, S.S. and Ahmad, S., 2017. Machine learning techniques for sentiment analysis: A review. Int. J. Multidiscip. Sci. Eng, 8(3), p.27.
[10] Gupta, B., Negi, M., Vishwakarma, K., Rawat, G., Badhani, P. and Tech, B., 2017. Study of Twitter sentiment analysis using machine learning algorithms on Python. International Journal of Computer Applications, 165(9), pp.29-34.
[11] Arulmurugan, R., Sabarmathi, K.R. and Anandakumar, H.J.C.C., 2019. Classification of sentence level sentiment analysis using cloud machine learning techniques. Cluster Computing, 22(1), pp.1199-1209.
[12] Renault, T., 2020. Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages. Digital Finance, 2(1), pp.1-13.
[13] Mukhtar, N., Khan, M.A. and Chiragh, N., 2018. Lexicon-based approach outperforms Supervised Machine Learning approach for Urdu Sentiment Analysis in multiple domains. Telematics and Informatics, 35(8), pp.2173-2183.
[14] Yang, P. and Chen, Y., 2017, December. A survey on sentiment analysis by using machine learning methods. In 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) (pp. 117-121). IEEE.
[15] van Atteveldt, W., van der Velden, M.A. and Boukes, M., 2021. The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms. Communication Methods and Measures, 15(2), pp.121-140.
[16] Wang, Y., Chen, Q., Shen, J., Hou, B., Ahmed, M. and Li, Z., 2021. Aspect-level sentiment analysis based on gradual machine learning. Knowledge-Based Systems, 212, p.106509.
[17] Mendon, S., Dutta, P., Behl, A. and Lessmann, S., 2021. A Hybrid approach of machine learning and lexicons to sentiment analysis: enhanced insights from twitter data of natural disasters. Information Systems Frontiers, pp.1-24.
[18] Naresh, A. and Venkata Krishna, P., 2021. An efficient approach for sentiment analysis using machine learning algorithm. Evolutionary Intelligence, 14, pp.725-731.
[19] El-Affendi, M.A., Alrajhi, K. and Hussain, A., 2021. A novel deep learning-based multilevel parallel attention neural (MPAN) model for multidomain arabic sentiment analysis. IEEE Access, 9, pp.7508-7518.
[20] S. MuthuKumaran, P.Suresh, A unified framework of sentimental analysis for online product reviews using enhanced ant colony optimization algorithm, International Journal of Pure and Applied Mathematics, Vol. 119, No. 4, 2018, pp. 489-496.
[21] Dorigo, M. and Stützle, T., 2019. Ant colony optimization: overview and recent advances. Handbook of metaheuristics, pp.311-351.
[22] Rahman, C.M. and Rashid, T.A., 2019. Dragonfly algorithm and its applications in applied science survey. Computational Intelligence and Neuroscience, 2019.
| Style | # |
|---|---|
| MLA | Mohammed K. Hassan, Dina K. Hassan, Ahmed K. Metawee, Bassem Hassan. "An Optimal Clustering with Hybrid Metaheuristic Algorithm for Sentiment Analysis and Classification." American Journal of Business and Operations Research, Vol. 4, No. 2, 2021 ,PP. 49-56 (Doi : https://doi.org/10.54216/AJBOR.040201) |
| APA | Mohammed K. Hassan, Dina K. Hassan, Ahmed K. Metawee, Bassem Hassan. (2021). An Optimal Clustering with Hybrid Metaheuristic Algorithm for Sentiment Analysis and Classification. Journal of American Journal of Business and Operations Research, 4 ( 2 ), 49-56 (Doi : https://doi.org/10.54216/AJBOR.040201) |
| Chicago | Mohammed K. Hassan, Dina K. Hassan, Ahmed K. Metawee, Bassem Hassan. "An Optimal Clustering with Hybrid Metaheuristic Algorithm for Sentiment Analysis and Classification." Journal of American Journal of Business and Operations Research, 4 no. 2 (2021): 49-56 (Doi : https://doi.org/10.54216/AJBOR.040201) |
| Harvard | Mohammed K. Hassan, Dina K. Hassan, Ahmed K. Metawee, Bassem Hassan. (2021). An Optimal Clustering with Hybrid Metaheuristic Algorithm for Sentiment Analysis and Classification. Journal of American Journal of Business and Operations Research, 4 ( 2 ), 49-56 (Doi : https://doi.org/10.54216/AJBOR.040201) |
| Vancouver | Mohammed K. Hassan, Dina K. Hassan, Ahmed K. Metawee, Bassem Hassan. An Optimal Clustering with Hybrid Metaheuristic Algorithm for Sentiment Analysis and Classification. Journal of American Journal of Business and Operations Research, (2021); 4 ( 2 ): 49-56 (Doi : https://doi.org/10.54216/AJBOR.040201) |
| IEEE | Mohammed K. Hassan, Dina K. Hassan, Ahmed K. Metawee, Bassem Hassan, An Optimal Clustering with Hybrid Metaheuristic Algorithm for Sentiment Analysis and Classification, Journal of American Journal of Business and Operations Research, Vol. 4 , No. 2 , (2021) : 49-56 (Doi : https://doi.org/10.54216/AJBOR.040201) |