# DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media
## 3 Methodology
### Feature Engineering  
Term Frequency-Inverse Document Frequency (TF-IDF)  
One of the common NLP feature extraction approaches is Term Frequency-Inverse Document Frequency (TF-IDF), which has been defined by Equation (14). Here, \(i\) is the string and \(j\) is the word. The term frequency, \(x_{i,j}\), represents the weight of a particular word \(j\) from the \(i\) feedback. The inverse term frequency is \(\text{idf}(w_{j})\). The mathematical model of TF-IDF is defined in Equation (14) and expresses the feedback from the stakeholders as a vector of term frequencies. The inverse part carries the information related to the importance of the word in the document [32].  
\[x_{i,j}=\text{tf}(w_{j},d_{i})\times\text{idf}(w_{j}) \tag{14}\]  
The Bag of Words (BoW) is another frequently used feature extraction method in NLP. It represents the document as the vector of word frequencies. A vector dimension represents each word in the vocabulary, whereas the value of the vector is the word frequency in the experimenting feedback. The BoW follows the mathematical operation defined in Equation (15) [33].  
\[x_{i,j}=\text{freq}(w_{j},d_{i}) \tag{15}\]  
In Equation (15), \(x_{i,j}\) represents the frequency of words in the stakeholders' feedback. Here, \(i\) is the number of frequencies and \(j\) is the document. The word is expressed as \(w_{j}\) in \(\text{freq}(w_{j},d_{i})\) and \(d_{i}\) means the \(i\)th document.  
The Word Embedding (WE) method used in this paper has been defined by Equation (16) as a Word2 Vec approach. It has been used to map words or phrases to real numbers. These vectors represent the semantic meaning of the words. It also allows for mathematical operation among the words. The embedding function in Equation (16) converts the input word \(w_{j}\) into a vector. Eventually, it converts every word into vector \(x_{i,j}\), where \(i\) is the vector of the \(i\) document [34].  
\[x_{i,j}=\text{emb}(w_{j}) \tag{16}\]  
Feature Engineering Method Selection  
In this study, the Word2 Vec method has been selected as the feature extractor. The DL model used in this paper is a Bidirectional Long Short-Term Memory (BiLSTM) network [35]. The Word2 Vec generates vector spaces and aligns the semantically similar words. That means it is a semantic-aware feature extraction method. As a result, the BiLSTM network gains context-aware classification capabilities when the Word2 Vec method is used [36]. Moreover, the vector space formed by Word2 Vec is richer in information than the one-hot-encoded vectors. It reduces the dimensionality, making it computationally more efficient, and the BiLSTM network learns faster. Additionally, the Word2 Vec approach is a continuous representation of the vectors, which makes mathematical operation among words easier. It becomes beneficial when the BiLSTM network is coupled with Word2 Vec, because the gates and memory cells of this network rely on mathematical operations [37]. Finally, the BiLSTM network captures patterns from both the forward and backward directions. When combined with the contextual embeddings from Word2 Vec, it becomes potent in understanding the nuances in the data. Considering the advantages of using Word2 Vec with the BiLSTM network, it has been used as a feature extractor from the dataset to train the DL model.