# DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media
## 2 Literature Review  
The underlying technology behind the proposed DPMS is a human sentiment classifier developed using a BiLSTM network. [PERSON] et al. [5], [PERSON] et al. [6], [PERSON] [7], and [PERSON] et al. [8] researched a university promotional management system. However, none of them use a Deep Learning (DL)-based automatic approach. It is a significant research gap that the proposed DPMS has filled. Although there is no data-driven promotional management system for HEIs, plenty of research has been conducted on sentiment analysis and the BiLSTM network, which are the underlying technology of the proposed DPMS. [PERSON] et al. [9] developed an aspect-based sentiment classifier using a Deep Convolutional Neural Network (DCNN) and the BiLSTM network. It achieved an accuracy of 93.28%. The combination of BiLSTM and Bidirectional Encoder Representations from Transformers (BERT) developed by [PERSON] et al. [10] classifies sentiment from SemEval2014 datasets [11] with an accuracy of 80.78%. [PERSON] et al. [12] analyzed the performance of sentiment classification on a small Apple dataset using a hybrid BERT model and achieved an accuracy of 91.72%. The proposed DPMS's sentiment classification accuracy is 98.12%, which is much better than these DL methods. The application of DMPS is Social Media Marketing (SMM). [PERSON] et al. [13] developed a Machine Learning (ML)-based approach to enhance social media marketing, which achieves an average accuracy of 86.6%. [PERSON] et al. [14] applied ML to target customers through social media post mining. This approach reached an accuracy of up to 82%. A reinforcement learning-based approach developed by [PERSON] et al. [15] analyzes the effects of advertisements. The review of the recent literature ensures that the proposed DPMS is unique and that none of the concurrent approaches is similar. The proposed DPMS is a BiLSTM network-based sentiment classification-centric approach. [PERSON] et al. [16] used a BiLSTM-based sentiment classifier using people's tweets on the Ukraine-Russia war. This approach obtains an accuracy of 91.79%. [PERSON] et al. [17] focuses on emotional classification using sentiment analysis with the BERT and BiLSTM network combination. It achieved an accuracy of 85.44%. [PERSON] et al. [18] worked on the same objective--emotion classification. Their CNN-BiLSTM-based approach achieved an accuracy of 92.85%. Although the proposed DPMS's technology is similar to these approaches, the application is unique. To our best knowledge, this is the first approach of applying the BiLSTM network to identify stakeholders' sentiments and to make social media promotion more effective. Research Gap AnalysisThe literature review and summary are in Table 1 demonstrate that sentiment analysis using the BiLSTM network and machine learning models is a well-developed field of research. It is also noticeable that the university's promotional management strategy is an active research field. Applying a sentiment classifier built using the BiLSTM network in developing a data-driven promotional management system is an innovative approach. However, it is a significant research gap that has not been addressed yet. The proposed DPMS has been developed to fill this research gap. Social Media Marketing (SMM) is now a mainstream marketing strategy. There are multiple machine-learning approaches that have been applied in SMM. However, the core focus of these methods is on data analytics and consumer behavior prediction. There is a research gap in increasing the impression using positive reactions and responses on social media with a data-driven approach. The proposed DPMS guides the social media marketing team in choosing effective content to promote, which minimizes negative reactions, increases positive responses, and contributes to better impressions.