## 3 The Proposed Model
### Data Pre-Processing  
The presented CLBEDC-SND model performed distinct levels of data pre-processing to make it compatible for further processing. In the social media data, there always exists unwanted parts where the pre-processing stage takes place for removing those parts from the comment that eventually assist in improving the performance [24]. In the study, this stage undergoes six distinct steps, namely stopping word removal, tokenization, punctuation removal, URL removal, and lemmatization. The pre-processing function can be carried out in a mathematical form:  
\[p_{r}=\lambda_{p}[I_{t}] \tag{1}\]  
In Equation (1), \(p_{r}\) indicates the output of pre-processing function. \(I_{t}\) shows the input dataset and \(\lambda_{p}\) is denotes pre-processing function as follows  
\[\lambda_{p}=\left[\lambda_{tk},\,\lambda_{pr},\,\lambda_{sr},\,\lambda_{lm}, \,\lambda_{ur}\right] \tag{2}\]  
In Equation (2), \(\lambda_{tk}\) denotes the tokenization function, \(\lambda_{pr}\) indicates the removal of punctuation function, \(\lambda_{sr}\) shows the removal of stop word function, \(\lambda_{lm}\) refers to the lemmatization function, and \(\lambda_{ur}\) indicates the removal of URL function.  
At first, the input text is subjected to the tokenization method where the whole text is detached into small unit named token. This technique assists the machine in easily understanding the text and it is expressed as follows:  
\[\lambda_{tk}=\left[I_{t1},\,I_{t2},\,I_{t3},\ldotsldots\,I_{tn}\right] \tag{3}\]  
The removal of the punctuation method is performed afterward the tokenization where the punctuation mark (\({}^{\prime}\)! \({}^{\prime\prime}\)! \({}^{\prime\prime}\)!) is detached from the tokenized dataset for a further examination of the text and it is shown in the following  
\[\lambda_{p_{1}}=\lambda_{pr}[I_{t}] \tag{4}\]  
Stop word is the most commonly used word that barely contributes all meaning in the language such as \"the\", \"a\", \"is\", \"are\", \"or\" and so on. The word has small amount of data for analyzing emotion from the texts that word was removed such that the quality of text is optimized.  
Figure 1: Working process of CLBEDC-SND approach.  
The lemmatization procedure takes place afterward the removal of stop words where the root word is analyzed. The root word is just a meaningful base form of the text named the lemma. For instance, excites, excited, exciting, excitement shows a similar meaning where the lemma is root word or \"excite\".  
Lastly, the URL removal procedure is carried out. It indicates the location of resource that does not give any essential data for analyzing sentiment.  
The checking of incomplete dataset is completed, even a smaller amount of incomplete dataset might cause misprediction of emotion in the text to manage these problems. This is the point where they check the existence of irrelevant or incomplete datasets from the pre-processed dataset. When any incomplete dataset is existent, it replaces that dataset with complete meaningful dataset such that the information is correctly synthesized and provides significant emotion.  
For instance, \(c\)\(u\) tmrw\({}^{\prime}\) is replaced by \"see you tomorrow\", \"gd mrng\" is replaced by \"good morning\", \"b4\" is replaced by \"before\", \"2 day\" is replaced by \"today\", \"LoI\" is replaced by \"Laugh out loud\". Subsequently, the completed dataset undergoes process of feature extraction.