## 3 Methodology
### Classification Methods  
#### 3.3.1 Random Forest (RF)  
The RF Algorithm, which was first presented in the literature in [45], performs classification by training each decision tree on a different observation sample and producing various models.  
These are the steps involved in the Random Forest (RF) algorithm:  
1. Random samples are selected from the input dataset.
2. The algorithm constructs a decision tree that will yield the prediction result for each selected sample.
3. This model is used in the classification problem for each of the predicted outcomes.
4. The prediction that receives the most votes is the final result.  
The definition of the k trained decision trees in the Random Forest model is given in Equation (1), below.  
\[H\big{(}X,\theta_{j}\big{)}=\sum_{i=0}^{k}h_{i}\big{(}x,\theta_{j}\big{)}, \qquad(j=1,2,3,\ldots,m) \tag{1}\]  
The general architecture of the RF, depicting this situation, is illustrated in Figure 4.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Word Embedding Method** & **Advantages** & **Disadvantages** \\ \hline \multirow{6}{*}{**TF-IDF**} & The algorithm is simple to use, as it is & TF-IDF cannot assist in carrying semantic \\  & computationally efficient, cost effective to run, & meaning. \\  & and provides a clear basis for similarity & TF-IDF disregards word order. \\ \hline \multirow{6}{*}{**Word2 Vec**} & It is a computationally efficient method for & Word2 vec does not handle polysemy very well, \\  & generating word embeddings. & which is the phenomenon of a single word \\  & It produces embeddings that capture semantic & having multiple meanings. \\  & relationships between words. & Word2 vec requires a large corpus of text to \\  & It employs dimensionality reduction & train on, and it may not perform well on words \\  & techniques to compress the high-dimensional & that are not present in the training data. \\  & vector space of words into a lower-dimensional & While word2 vec produces vector \\  & space. & representations of words, it can be difficult to \\  & It is flexible and can be trained on different & interpret what these vectors actually represent. \\  & datasets. & While word2 vec is generally computationally \\  & Word2 vec can be used for language modeling, & efficient, it can still require significant \\  & which involves predicting the likelihood of a & computing resources, especially when training \\  & sequence of words occurring in a text corpus. & on large datasets or with complex models. \\ \hline \multirow{6}{*}{**Doc2 Vec**} & Doc2 vec can generate vector representations & Doc2 vec is a more complex model than \\  & that capture the semantic relationships & traditional bag-of-words models, and can be \\  & between entire documents, making it useful for & more difficult to implement and interpret. \\  & tasks such as document classification, & Doc2 vec can require significant computing \\  & clustering, and similarity searches. & resources, especially when trained on large \\  & Unlike traditional bag-of-words models, & datasets or with complex models. \\  & doc2 vec can handle variable-length documents, & The quality of the document embeddings \\  & which makes it useful for processing long and & produced by doc2 vec depends on the quality \\  & complex documents. & and size of the training data. \\  & Doc2 vec can incorporate the context of a & While doc2 vec produces vector representations \\  & document, including the surrounding & of documents, it can be difficult to interpret \\  & documents and other relevant information, & what these vectors actually represent. This can \\  & into the vector representation of the document. & make it challenging to understand why certain \\  & Doc2 vec is computationally efficient and can & documents are more similar to each other than \\  & be used on large datasets. & others. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.