## 2 Sentiment Analysis Algorithms
### Deep Learning Approach  
Deep learning has emerged as a popular method for sentiment analysis due to its ability to learn representations of textual data. In these methods, textual data is preprocessed and then encoded using pretrained embeddings such as GloVe and word2 vec. These embeddings are then fed into deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) for representation learning and classification. In [PERSON] and [PERSON]'s study (2017) [22], sentiment analysis was performed on a dataset of 4000 Tweets in both Korean and English. The preprocessing phase included tokenization, case folding, stemming, and the removal of stop words, numbers, and punctuation marks. A multilayer perceptron (MLP) model with three hidden layers was used in the study, which was optimized using Stochastic Gradient Descent (SGD). The results showed that the MLP model achieved an accuracy of 75.03% on the dataset. Similarly, in [PERSON] et al. 's study (2019) [23], an MLP model was used for sentiment analysis on Turkish Tweets. The dataset consisted of 3000 positive and negative Tweets with the hashtag \"15 Temmuz\". The preprocessing phase included Turkish deacification, tokenization, stop-word and punctuation removal, and stemming. The word2 vec pretrained model was used to convert the text into embeddings, which were then used as inputs to the MLP consisting of six dense layers and three dropout layers. The results showed that the MLP achieved an accuracy of 81.86% on the dataset. \begin{table}
\begin{tabular}{c c c c c} \hline
**Literature** & **Features** & **Classifier** & **Dataset** & **Accuracy (\%)** \\ \hline [PERSON] et al. (2016) [4] & MNB & Sentiment140 & 85 \\ \hline [PERSON] et al. (2018) [5] & NB & Self-collected dataset (from Tweets) & 77 \\ \hline [PERSON] et al. (2018) [6] & A priori algorithm & NB, SVM & Self-collected dataset (from Amazon) & 83.42 \\ \hline [PERSON] et al. (2018) [7] & Unigram, Bigram & NB, SVM, ME & IMDb & 88 \\  & & Sentiment140 & 90 \\ \hline [PERSON] et al. (2018) [8] & TF-IDF & DT & \begin{tabular}{c} Sentiment140, Polarity Dataset, and \\ University of Michigan dataset \\ \end{tabular} & 84 \\  & & AdaBoost & & 67 \\  & SVM & & 82 \\ \hline [PERSON] and & & & \\ [PERSON] (2018) [10] & AdaBoost & Indian Airlines & 84.5 \\ \hline [PERSON] et al. (2018) [9] & Regression Tree & Own dataset & 88.99 \\ \hline [PERSON] et al. (2019) [11] & SVC & Airline review & 82.48 \\  & MNB & & 76.56 \\ \hline [PERSON] et al. (2019) [12] & TF-IDF & SVM & Tweets related to homosexuals & 99.5 \\ \hline [PERSON] and [PERSON] (2019) [13] & NB & \
\begin{tabular}{c} Twitter (2019 presidential candidates of \\ the Republic of Indonesia) \\ \end{tabular} & 75.58 \\ \hline [PERSON] (2019) [14] & SVM & Twitter (Indian Railways) & 91.5 \\ \hline [PERSON] et al. (2019) [15] & TF-IDF & Neural Network & Sentiment140 & 80 \\ \hline [PERSON] et al. (2019) [16] &
\begin{tabular}{c} AdaBoost (Bagging and \\ Boosting) \\ \end{tabular} & Skytrax and Twitter (Airlines) & 68 F-score \\ \hline [PERSON] et al. (2019) [17] & TF-IDF & Ridge Classifier & IMDb & 90.54 \\  & Sentiment 140 & 76.84 \\ \hline [PERSON] (2020) [18] & TF-IDF & MNB & Arabic Tweets & 87.5 \\ \hline [PERSON] et al. (2020) [19] & Bag of Words & SVM & Twitter US Airline Sentiment & 83.31 \\ \hline [PERSON] et al. (2021) [20] & TF-IDF & SVM & Self-collected dataset & 78.25 \\ \hline [PERSON] et al. (2021) [21] & NB & NLTK corpus & 99.73 \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of machine learning approaches. In [PERSON] et al. (2021) [24], an MLP was utilized to perform sentiment analysis on COVID-19-related Tweets. The dataset consisted of 101,435 Tweets and was preprocessed to remove HTML tags and non-letters and to tokenize and stem the text.