# DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media
###### Abstract  
SocialMedia Marketing (SMM) has become a mainstream promotional scheme. Almost every business promotes itself through social media, and an educational institution is no different. The users' responses to social media posts are crucial to a successful promotional campaign. An adverse reaction leaves a long-term negative impact on the audience, and the conversion rate falls. This is why selecting the content to share on social media is one of the most effective decisions behind the success of a campaign. This paper proposes a Data-Driven Promotional Management System (DPMS) for universities to guide the selection of appropriate content to promote on social media, which is more likely to obtain positive user reactions. The main objective of DPMS is to make effective decisions for Social Media Marketing (SMM). The novel DPMS uses a well-engineered and optimized BiLSTM network, classifying users' sentiments about different university divisions, with a stunning accuracy of 98.66%. The average precision, recall, specificity, and F1-score of the DPMS are 98.12%, 98.24%, 99.39%, and 98.18%, respectively. This innovative Promotional Management System (PMS) increases the positive impression by 68.75%, reduces the adverse reaction by 31.25%, and increases the conversion rate by 18%. In a nutshell, the proposed DPMS is the first promotional management system for universities. It demonstrates significant potential for improving the brand value of universities and for increasing the intake rate. deep learning; BiLSTM network; promotional management system; social media marketing; decision support system; data-driven decision +
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The proposed Data-Driven Promotional Management System (DPMS) is trained to utilize stakeholders' feedback on various university divisions, including faculties, the library, the admissions office, finance and accounts, human resource management, and the registrar's office. This feedback is collected and refined through the application of Natural Language Processing (NLP) techniques that are well-suited for analyzing such data [3]. Subsequent to the collection process, this study undertakes extensive feature engineering to ascertain the most productive feature extraction methods [4]. Given the nature of the dataset features, a Bidirectional Long Short-Term Memory (BiLSTM) network is selected for crafting the data-centric promotional management system tailored for university settings. The BiLSTM network is meticulously architected to categorize users' feedback into four distinct classes, facilitating the extraction of valuable insights into stakeholders' experiences across the various university divisions. Based on these insights, promotional posts are crafted to accentuate the divisions that elicit positive experiences from users. This novel DPMS has the potential to revolutionize universities' promotional management systems. It is a unique application of the BiLSTM network in Natural Language Processing (NLP)-based social media marketing. The novelties and outstanding contributions of this system are listed below. * Novel Concept: The concept of applying the BiLSTM network to develop a data-driven decision-making system for universities on social media is the first of its kind. * Effective Network Design: The BiLSTM network to design the DPMS has been carefully engineered and optimized to maximize the stakeholders' feedback dataset performance. * Outstanding Performance: The DPMS demonstrates outstanding performance, with an average validation accuracy of 98.66%. Its precision, recall, specificity, and F1-score are 98.12%, 98.24%, 99.39%, and 98.18%, respectively. * Improving Social Media Marketing Impression: This exceptional promotional management system demonstrates an increase in positive impressions by 68.75% and reduces the negative responses by 31.25% in real-world settings. This paper presents a novel application of the BiLSTM network in NLP by developing a data-driven promotional management system for universities.