Analyzing the opinions and emotions of Internet customers using deep ensemble learning based on rbm
Social Determinants of Health,
Vol. 9 (2023),
1 January 2023
https://doi.org/10.22037/sdh.v9i1.41964
Abstract
Background: The emotions and opinions of Internet users are critical, as they directly influence the provision of proper services. The aim of this study was analyzing the opinions and emotions of internet customers using deep ensemble learning based on rbm.
Methods: Method of this study was based on the deep ensemble learning technique which uses a deep ensemble neural network based on Gaussian restricted Boltzmann machine and cost-sensitive tree the opinions and emotions of Internet customers were analyzed in terms of semantics and linguistics in virtual shops. To analyze behavior or emotions, existing algorithms were divided into groups of semantic approach, language approach and machine learning. The semantic, linguistic and group learning aspects (machine learning) were considered together. The opinions, feelings, and behaviors of Internet customers were analyzed. The proposed method was implemented in MATLAB software. To evaluate this method, conventional criteria that were /applied in data mining applications have been used including accuracy, recall, and F score.
Results: Based on the experiments performed and by evaluating this method against individual and ensemble methods plus the approaches presented in data mining so far, it was revealed that the proposed model outperforms other methods regarding data mining assessment criteria.
Conclusion: Based on social engineering, the suggested model is provided to forecast consumer behavior. In addition to analyzing customers' behavior which examined their emotions and feelings based on their opinions. The results of this study can be used by planners in the field of competitive internet markets.
- Data Mining
- Deep Learning
- Emotions
- Internet
- Sentiment Analysis
- Social Networking
How to Cite
References
2. Keshwani K, Agarwal P, Kumar D. Prediction of Market Movement of Gold, Silver and Crude Oil Using Sentiment Analysis. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 554. Springer, Singapore;2018. https://doi.org/10.1007/978-981-10-3773-3_11
3. Luo X, Jiang C, Wang W, Xu Y, Wang J, Zhao W. User behavior prediction in social networks using weighted extreme learning machine with distribution optimization. Future Generation Computer Systems. 2019;93(1):1023-1035. https://doi.org/10.1016/j.future.2018.04.085
4. Bertola F, Patti V. Ontology-based affective models to organize artworks in the social semantic web. Information Processing & Management. 2016;52(1):139-162. https://doi.org/10.1016/j.ipm.2015.10.003
5. Jain Y, Tiwari N, Dubey S. Jain S. A comparative analysis of various credit card fraud detection techniques. International Journal of Recent Technology and Engineering. 2019;7(2):402-407. https://www.researchgate.net/publication/332264296_A_comparative_analysis_of_various_credit_card_fraud_detection_techniques
6. Chen L, Qi L. Social opinion mining for supporting buyers’ complex decision making: exploratory user study and algorithm comparison. Social Network Analysis and Mining. 2021;1(4):301-320. https://doi.org/10.1007/s13278-011-0023-y
7. Hu Y, Boyd-Graber JL, Satinoff B, Smith-Renner A. Interactive topic modeling. Machine Learning. 2014;95(3):423-469. https://doi.org/10.1007/s10994-013-5413-0
8. Pavlou PA, Fygenson M. Understanding and Predicting Electronic Commerce Adoption: An Extension of the Theory of Planned Behavior. MIS Quarterly. 2006;30(1):115-143. https://doi.org/10.2307/25148720
9. Yi S, Liu X. Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review. Complex & Intelligent Systems. 2020;6(5):621-634. https://doi.org/10.1007/s40747-020-00155-2
10. Khalid MA, Ashraf I, Mehmood A, Ullah S, Ahmad M, Choi GS. GBSVM: Sentiment Classification from Unstructured Reviews Using Ensemble Classifier. Applied Sciences. 2020;10(8):2788-2798. https://doi.org/10.3390/app10082788
11. Sadhasivam J, Babu R. Sentiment Analysis of Amazon Products Using Ensemble Machine Learning Algorithm. International Journal of Mathematical, Engineering and Management Sciences. 2019;4(2):508-520. https://doi.org/10.33889/IJMEMS.2019.4.2-041
12. Kang M, Ahn JC, Lee KS. Opinion mining using ensemble text hidden Markov models for text classification. Expert Systems. 2018;94(1):218-227. https://doi.org/10.1016/j.eswa.2017.07.019
13. Severyn A, Moschitti A, Uryupina O, Plank B, Filippova K. Multi-lingual Opinion Mining on YouTube. Information Processing & Management. 2015;52(1):46-60. https://doi.org/10.1016/j.ipm.2015.03.002
14. Balazs JA, Velásquez JD. Opinion Mining and Information Fusion: A survey. Information Fusion. 2016;27(1):95-110. https://doi.org/10.1016/j.inffus.2015.06.002
15. Rill S, Reinel D, Scheidt J, Zicari RV. PoliTwi: Early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis. Knowledge-Based Systems. 2014;69(1):24-33. https://doi.org/10.1016/j.knosys.2014.05.008
16. Chen Z, Liu B. Mining topics in documents: standing on the shoulders of big data. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data. 2014;2(1):1116-1125. https://doi.org/10.1145/2623330.2623622
17. Nakov P, Rosenthal S, Kiritchenko S, Mohammad SM, Kozareva Z, Ritter A, Stoyanov V, Zhu X. Developing a successful SemEval task in sentiment analysis of twitter and other social media texts. Language Resources and Evaluation. 2016;50(1):35-65. https://doi.org/10.1007/s10579-015-9328-1
18. Poria S, Cambria E, Gelbukh A. Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems. 2016;108(1):42-49. https://doi.org/10.1016/j.knosys.2016.06.009
- Abstract Viewed: 112 times
- PDF Downloaded: 72 times