پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 647 |
| تعداد مقالات | 9,474 |
| تعداد مشاهده مقاله | 68,297,332 |
| تعداد دریافت فایل اصل مقاله | 47,817,795 |
Predicting the number of comments on facebook posts using an ensemble regression model | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 49-62 اصل مقاله (380.65 K) | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.4796 | ||
| نویسندگان | ||
| Omid Rahmani Seryasat* 1؛ Isaac Kor2؛ Hossein Ghayoumi Zadeh3؛ Arash shams Taleghani4 | ||
| 1Assistant Professor, Department of Electrical Engineering, Shams Higher Education Institute, Iran. | ||
| 2Department of Computer Engineering, Faculty of Technical Engineering, Shams institute of Higher Education, Gonbad Kavous, Iran. | ||
| 3Assistant Professor of Biomedical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran. | ||
| 4Assistant Professor, Aerospace Research Institute, Ministry of Science, Research and Technology, Iran. | ||
| تاریخ دریافت: 10 تیر 1399، تاریخ بازنگری: 14 مهر 1399، تاریخ پذیرش: 01 بهمن 1399 | ||
| چکیده | ||
| The nature and importance of user’s comments in various social media systems play an important role in creating or changing people's perceptions of certain topics or popularizing them. It has now an important place in various fields, including education, sales, prediction, and so on. In this paper, Facebook social network has been considered as a case study. The purpose of this study is to predict the volume of Facebook users' comments on the published content called post. Therefore, the existing problem is classified as a regression problem. In the method presented in this paper, three regression models called elastic network, M5P model, and radial basis function regression model are combined and an ensemble model is made to predict the volume of comments. In order to combine these base models, a strategy called stack generalization is used, based on which the output of the base models is provided to a linear regression model as new features. This linear regression model combines the outputs of the 3 base models and determines the final output of the system. To evaluate the performance of the proposed model, a database of the UCI dataset, which has 5 training sets and 10 test sets, has been used. Each test set in this database has 100 records. In the present study, the efficiency of the base models and the proposed ensemble model is evaluated on all these sets. Finally, it is concluded that the use of the ensemble model can reduce the average correlation coefficient (as one of the evaluation criteria of the model) to 74.4 ± 16.4, which is an acceptable result. | ||
| کلیدواژه‌ها | ||
| Regression؛ Ensemble Model؛ Facebook؛ Comment Volume Prediction | ||
| مراجع | ||
|
[1] K. Buza, L. Schmidt-Thieme, & R. Janning (Eds.), Feedback Prediction for Blogs. In M. Spiliopoulou, Data Analysis, Machine Learning and Knowledge Discovery (pp. 145-152). Cham: Springer International Publishing.(2014). [2] M. Ghane, AR. Nejad, M. Blanke, Z. Gao, T. Moan, Statistical fault diagnosis of wind turbine drivetrain applied to a 5MW floating wind turbine, Journal of Physics: Conference Series 753 (5), (2017). [3] M. Ghane, MJ. Tarokh, Multi-objective design of fuzzy logic controller in supply chain, Journal of Industrial Engineering International 8 (1), 1-8. [4] M. Ghane, M. Zarvandi, MR. Yousefi, attenuating bullwhip effect using robust-intelligent controller, 2010 5th IEEE International Conference Intelligent Systems, 309-314. [5] H.Ghayoumi Zadeh , A. Montazeri ,I. Abaspur Kazerouni , J. Haddadnia , Clustering and screening for breast cancer on thermal images using a combination of SOM and MLP. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2017 Jan 2;5(1):68-76. [6] S. Jamali, H. Rangwala, Digging Digg: Comment Mining, Popularity Prediction, and Social Network Analysis. Paper presented at the International Conference on Web Information Systems and Mining, (2009). [7] E. Koozegar, M. Soryani, and I. Domingues. ”A New Local Adaptive Mass Detection Algorithm in Mammograms.” BIOSIGNALS. 2013. [8] E. Kozegar, ”Computer aided detection in automated 3-D breast ultrasound images: a survey.” Artificial Intelligence Review (2019): 1-23. [9] M. M. Rahman, Intellectual knowledge extraction from online social data. Paper presented at the Informatics, Electronics Vision (ICIEV), (2012). [10] O. RahmaniSeryasat, J. Haddadnia. ”Evaluation of a new ensemble learning framework for mass classification in mammograms.” Clinical breast cancer 18.3 (2018): e407-e420. [11] O. RahmaniSeryasat, J. Haddadnia, H. Ghayoumi-Zadeh, A new method to classify breast cancer tumors and their fractionation. Ciˆencia e Natura, 37(4), (2015), 51-57. [12] O. RahmaniSeryasat, J. Haddadnia, H. Ghayoumi Zadeh, Assessment of a Novel Computer Aided Mass Diagnosis System in Mammograms, Iranian Journal of Breast Disease 9 (3), ( 2016),31-41. [13] O. RahmaniSeryasat, J. Haddadnia, ”Assessment of a novel computer aided mass diagnosis system in mammograms.” Biomedical Research (2017) Volume 28, Issue 7. [14] A. Salmasi,A. Shadaram, A.S. Taleghani, Effect of plasma actuator placement on the airfoil efficiency at poststall angles of attack, IEEE Transactions on Plasma Science, 2013, 41(10), pp. 3079–3085, 6601652. [15] S.M.Sheikholeslam Noori, M. Taeibi Rahni, S.A. Shams Taleghani, Multiple-relaxation time color-gradient lattice Boltzmann model for simulating contact angle in two-phase flows with high density ratio, European Physical Journal Plus, 2019, 134(8), 399. [16] K. Singh, R. Kaur, &D. Kumar, Comment Volume Prediction Using Neural Networks and Decision Trees. Paper presented at the Proceedings of the 2015 17th UKSIM-AMSS International Conference on Modelling and Simulation, (2015). [17] S. Negi, & S. Chaudhury, Predicting User-to-content Links in Flickr Groups. Paper presented at the Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), (2012).[18] A.S. Taleghani, A. Shadaram, M. Mirzaei, S. Abdolahipour, Parametric study of a plasma actuator at unsteady actuation by measurements of the induced flow velocity for flow control, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2018, 40(4), 173. [19] M. Tsagkias,W. Weerkamp, & M. d. Rijke, Predicting the volume of comments on online news stories. Paper presented at the Proceedings of the 18th ACM conference on Information and knowledge management, Hong Kong, China, (2009). [20] M. Tsagkias, W. Weerkamp, &M. de Rijke, News Comments:Exploring, Modeling, and Online Prediction. In C. Gurrin, Y. He, G. Kazai, U. Kruschwitz, S, (2010). [21] T. Yano ,NA. Smith, What’s Worthy of Comment? Content and Comment Volume in Political Blogs. Paper presented at the Proceedings of the International AAAI Conference on Weblogs and Social Media, Washington, DC, (2010). [22] I. Zare, A. Ghafarpour, H. Ghayoumi Zadeh, J. Haddadnia, and M. Mostafavi Isfahani. ”Evaluating the thermal imaging system in detecting certain types of breast tissue masses.” (2016). [23] R. Zhang ,Z. Zhang , X. He, & A. Zhou, Dish Comment Summarization Based on Bilateral Topic Analysis. Paper presented at the 2015 IEEE 31st International Conference on Data Engineering, (2015, April). | ||
|
آمار تعداد مشاهده مقاله: 44,895 تعداد دریافت فایل اصل مقاله: 37,840 |
||