# DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media
## 3 Methodology
### Dataset Preparation
#### 3.1.2 Text Processing  
Tokenization  
The string of the dataset has been split into multiple tokens, which is the first step of the text processing in this paper. This process has been defined by Equation (4), where \(f(w_{i})\) is the function responsible for generating tokens. Only unique tokens are allowed after tokenization, and duplicate tokens are removed [23]. \[f(w_{i})=w_{i}\;\forall\;w_{i}\in T \tag{4}\]  
The manual inspection performed on the dataset shows multiple improper stakeholder responses. There are responses with meaningless sentences. In multiple instances, there are spelling mistakes and emojis. Moreover, there is some ambiguous feedback that contains no meaningful information. Furthermore, there are special characters such as punctuation marks, exclamatory symbols, numerical lists, and improper capitalization. It is essential to process this dataset to remove instances that are not useful for training the DL model. Uniform Letter-Casing  
The first step of the text processing of the proposed DPMS is to lowercase the letters. According to the English language standard, capitalizing is meaningful to human readers. However, it carries no information variation for computer-aided systems. Moreover, having lowercase and uppercase letter combinations makes the vector space complicated. That is why at the beginning of text processing, the letters have been made lowercase by following the mathematical model explained in Equation (5) [24]. \[low(D_{i})= \begin{cases}\text{lower}(D_{i}),&\text{if }D_{i}\in Cap\\ \text{otherwise}\end{cases}  \tag{5}\]  
In the uniform letter-casing defined in Equation (5), the \(low(D_{i})\) represents the function where \(D_{i}\) is the dataset, which is the stakeholders' feedback. If \(D_{i}\) belongs to capital letters, the function converts to a lowercase letter. The punctuation makes the contents more readable. However, it is applicable to human readers. Punctuation marks do not contain any useful features while training DL models and predicting the sentiments through Natural Language Processing (NLP). The punctuation marks removal process is defined by Equation (6) [25]. \[R_{p}(D_{i})= \begin{cases}D_{i},&\text{if }\;D_{i}\
otin P\text{ remove,}\\ \text{otherwise}&\end{cases}  \tag{6}\]  
In Equation (6), the \(R_{p}\) represents the punctuation removal function, which takes \(D_{i}\) as the input. If the character is not a punctuation mark, the input remains unchanged. However, if it belongs to punctuation marks \(P\), the character is removed. Handling Stop Words  
The stop words (for example, articles, prepositions, conjunctions, auxiliary verbs, etc. ), do not carry enough information for Natural Language Processing (NLP). However, their presence in the dataset adds additional vectors in the word vector and increases the complexities of the vector space without making it more effective. That is why removing stop words makes the sentiment classification process used in the DPMS more effective. It is performed using Equation (7) [26]. \[H_{s}(D_{i})= \begin{cases}D_{i},&\text{if }D_{i}\
otin S\text{ remove,}\\ \text{otherwise}&\end{cases}  \tag{7}\]  
\[S_{w}=\{xx\in\text{Stop-Words}\} \tag{8}\]  
The \(H_{s}(D_{i})\) represents the filtering function that checks the stop words available in the \(D_{i}\). The stop word has been listed as a set defined by Equation (8). If any word is found in the set of stop words, it is removed from the \(D_{i}\) string. Processing Special Characters and Numbers  
The dataset used in this experiment contains multiple special characters and numeral lists. These special characters and numbers are not useful in NLP. In this paper, these special characters and numbers have been removed to reduce the vector-space complexity and to make the sentiment classifier faster. The process of handling special characters and numbers is defined by Equation (9). Here, the function \(C_{sn}()\) takes the \(D_{i}\) and removes the special characters if \(D_{i}\in A\), where A is the set of special characters and numbers [27]. \[C_{sn}(D_{i})=