# DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media
## 7 Discussion and Conclusions  
Promotional activities are essential for most businesses. Business growth, brand value, revenue flow, and many other important business factors directly relate to promotional activities. A business can suffer from a significant loss and even go bankrupt if there is weakness or major mistakes in promotional activity [49]. The university is no different. Promoting universities to maintain good student intake rates, improve brand value, and to gain popularity is essential [50]. Instead of an intuition or experience-based approach, a data-driven promotional management system is more effective. A BiLSTM-based DPMS has been presented in this paper. Network design and implementation are simple tasks. However, designing the dataset structure and collecting the appropriate data are challenging, and these have been overcome in this paper. The data collection strategy presented in this paper collects feedback from the stakeholders from different university divisions and processes them using the standard Natural Language Processing (NLP) standard. And finally, the dataset is converted into vectors. These vectors are used to train the BiLSTM network, which classifies the feedback into Good, Average, Neutral, and Poor categories. Based on this classification, the content to promote on social media is selected.  
The methodology used in this paper achieves an accuracy of 98.66%. The precision, recall, specificity, and F1-score obtained in this paper are 98.12%, 98.24%, 99.39%, and 98.18%, respectively. These are the performances of the BiLSTM network. The stunning performance is revealed when the proposed DPMS is applied to real-world settings. The experiment in real-world settings shows that it increases the impression by 68.75%. After making the decision from the DPMS's classification, the negative reaction to the social media posts was reduced by 31.25%. This remarkable result proves the outstanding performance of the proposed data-driven promotional management system for universities on social media. However, this system suffers from several limitations. The DPMS designed for one institution does not guarantee that it will be effective for others. This is because different institutions have different types of strengths and weaknesses. That is why it is less likely to obtain similar feedback. The observational period of the DPMS experiment is short; it cannot defend against AML attack, and the quality of feedback is uncontrollable. These are a few of the other weaknesses of this approach. This limitation paves the path toward conducting subsequent research to develop a better version of the DPMS that will have a more positive impact on successful social media marketing strategies.  
Conceptualization, M.E.H.; Methodology, M.E.H.; Validation, M.W.; Formal analysis, I.M.; Writing--original draft, N.F.; Writing--review & editing, T.J.; Visualization, A.B.; Supervision, M.W. and A.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Australian Research Council Discovery Project, Re-Engineering Enterprise Systems for Microservices in the Cloud, under Grant DP190100314.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to organizational privacy.  
The authors declare no conflict of interest.