1 Affiliation : Department of web science, Syrian Virtual University, Syria
Email : firstname.lastname@example.org
2 Affiliation : email@example.com
Email : Associate Professor, American University in the Emirates, United Arab Emirates
3 Affiliation : Assistant Professor, Computer and Automation Engineering - Damascus University-Syria, & Faculty of informatic engineering, International Private University for Science and Technology, Syria
Email : firstname.lastname@example.org
4 Affiliation : Assistant Professor, Faculty of informatic engineering, International Private University for Science and Technology, Syria
Email : email@example.com
Traditional models for predicting future sales of a product or service are based on previous, not updated data, resulting in unsatisfactory and inaccurate forecasting results, meaning that the data used as inputs to the forecasting process is stable and not dynamic during the forecasting process.The research aims to leverage social media data by extracting features from Facebook platform (features are reactions to posts) and using them as input to the automated forecasting system to try to predict corporate revenues.Machine learning algorithms have been trained to predict returns according to pre-stored data and can be updated on demand, which means that the proposed forecasting system will work in a dynamic environment.The following algorithms were used to predict the profitability of new services and the one with the highest accuracy was selected: (Random Forest, DT, Gradient Boosting, K nearest neighbors, NB).The results showed that Random Forest algorithm is the one with the best accuracy, with an accuracy of 67%, and a slight correlation was observed between the interactions on the target post and the profitability of the service within the post.
Machine Learning; Social Media Marketing; Classification; Sentiment Analysis; Facebook graph API.
 Ray, A., Bala, P.K. and Jain, R. , Utilizing emotion scores for improving classifier performance
for predicting customer's intended ratings from social media posts, Benchmarking: An
International Journal, (2021), Vol. 28 No. 2, pp. 438-464.
 Zimmerman, J., Ng, D., & Tusing, M. (2020). Social media marketing all-in-one for dummies:
4th edition. Unabridged. [United States]: Tantor Audio.
 Matthew A. Russell, Mikhail Klassen. Mining the Social Web, 3rd Edition, O'Reilly Media, Inc.
 Kaur, Wandeep & Balakrishnan, Vimala & Rana, Omer & Sinniah, Ajantha. (2018). Liking,
Sharing, Commenting and Reacting on Facebook: User behaviors’ impact on Sentiment
Intensity. Telematics and Informatics. 39. 10.1016/j.tele.2018.12.005.
 Abdallah, N., Iqbal, H., Alkhazaleh, H,. Ibrahim, A., Zeki, T., Habli, M. & Abdallah, O. (2020).
Determinants of M-Commerce Adoption: An Empirical Study, Journal of Theoretical and
Applied Information Technology. May, (Vol. 98).
 Sepehr Forouzani .Using social media and machine learning to predict financial performance of
a company .uppsala unversitet / teknisk- naturvetenskaplig fakultet .)6102(.
 Matthias Bogaert, Michel Ballings, Dirk Van den Poel, Asil Oztekin, Box office sales and social
media: A cross-platform comparison of predictive ability and mechanisms, Decision Support
 Dipak Gaikar, Riddhi Solanki, Harshada Shinde, Pooja Phapale , Ishan Pandey .Movie Success
Prediction Using Popularity Factor from Social Media .International Research Journal of
Engineering and Technology (IRJET), page 6, 10Apr, 2019.
 Chavan, Sandeep & Panchal, Simsri & Sawant, Tanvi & Shinde, Janhavi. (2020). Predicting
Online Product Sales using Machine Learning. International Journal of Engineering Research.
 Purvika Bajaj ،Renesa Ray ،Shivani Shedge ،Shravani Vidhate, Prof. Dr. Nikhilkumar Shardoor .
June, 2020 .Sales Prediction Using Machine Learning Algorithms .International Research
Journal Of Engineering And Technology (IRJET).
 B. Senthil Arasu, B. Jonath Backia Seelan, N. Thamaraiselvan, A machine learning-based
approach to enhancing social media marketing, Computers & Electrical Engineering,Volume
 Gabor Szabo, Gungor Polatkan, P. Oscar Boykin, Antonios Chalkiopoulos. Social Media Data
Mining and Analytics, Wiley, October 2018,
 Duarte, J. J., Montenegro González, S., & Cruz, J. C. (2020). Predicting stock price falls using
news data: Evidence from the Brazilian market. Computational Economics.
 Cui, Ruomeng & Gallino, Santiago & Moreno, Antonio & Zhang, Dennis. The Operational
Value of Social Media Information. Production and Operations Management. (2017).
 Gogas, P., Papadimitriou, T. Machine Learning in Economics and Finance. Comput Econ 57, 1–
 Ali, M.H., Ibrahim, A., Wahbah, H., Al_Barazanchi, A. (2021). Survey on encode biometric data
for transmission in wireless communication networks, Periodicals of Engineering and Natural
Sciences. Vol 9 (4).
 Almajed, R., Ibrahim, A., Abualkishik, A., Mourad, N., Almansour, F. (2022). Using Machine
Learning Algorithm for Detection of Cyber-Attacks in Cyber Physical Systems. Periodicals of
Engineering and Natural Sciences.