## 3 Ensemble Learning Approach
\begin{table}
\begin{tabular}{c c c c c} \hline
**Literature** & **Embedding** & **Classifier** & **Dataset** & **Accuracy (\%)** \\ \hline \multirow{2}{*}{[PERSON] et al. (2020) [43]} & mBERT & Pakistan elections in 2018 & 69 \\  & & XLM-R & (Tweets) & 71 \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] (2021) [44]} & BERT & Sentiment140 & 85.4 \\  & & & & IMDb \\ \hline \multirow{2}{*}{[PERSON] et a. (2022) [45]} & \multirow{2}{*}{RoBERTa-LSTM} & IMDb & 92.96 \\  & & & Twitter US Airline Sentiment & 91.37 \\  & & & Sentiment140 & 89.70 \\ \hline \multirow{4}{*}{[PERSON] et al. (2022) [46]} & \multirow{4}{*}{BERT} & \multirow{4}{*}{CBRNN} & US airline reviews & 97 \\  & & & Self-driving car reviews & 90 \\  & & & US presidential election & 96 \\  & & & reviews & IMDb & 93 \\ \hline \multirow{3}{*}{[PERSON] et al. (2022) [47]} & \multirow{3}{*}{ST-GCN} & SST-GCN & SST-B & 95.43 \\  & & & IMDB & 94.94 \\  & & & Yelp 2014 & 72.7 \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] (2022) [48]} & \multirow{2}{*}{BERT} & \multirow{2}{*}{KEAHT} & COVID-19 vaccine & 91 \\  & & & Indian Farmer Protests & 81.49 \\ \hline \multirow{2}{*}{[PERSON] et al. (2022) [49]} & \multirow{2}{*}{Zero-shot transformer} & Ensemble learning & SemEval 2017 & 87.3 \\ \hline \multirow{2}{*}{[PERSON] et al. (2022) [50]} & \multirow{2}{*}{Transformer} & DST & Self-collected & 84 \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] (2022) [51]} & \multirow{2}{*}{Light-Transformer} & Light-Transformer & NLPCC2014 Task2 & 76.40 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._features. The ensemble model employing the majority voting method achieved the highest accuracy of 91% on the dataset. In another study, [PERSON] and [PERSON] (2020) [56] proposed an ensemble learning algorithm with stochastic gradient descent, logistic regression, naive Bayes, decision tree, random forest, and support vector machine. Three datasets, SemEval-2017 4A, SemEval-2017 4B, and SemEval-2017 4C, were used, which were preprocessed using tokenization, word substitution, and stemming, as well as stop-word, punctuation, number, and repeated-word removal. Five different features were extracted from the text, including N-grams, part-of-speech tagging, TF-IDF, and bag-of-words and lexicon-based features. The authors used two ensemble learning methods, simple-majority voting ensemble and weighted-majority voting ensemble, in the experiments. The results showed that the ensemble model with weighted-majority voting achieved the highest accuracy of 72.95%, 90.8%, and 68.89% on the SemEval-2017 4A, SemEval-2017 4B, and SemEval-2017 4C datasets, respectively. [PERSON] et al. (2020) [57] conducted a study on the effectiveness of machine learning techniques for sentiment analysis, including naive Bayes, logistic regression, stochastic gradient descent, and an ensemble model using majority voting. The Sentiment140 dataset was preprocessed by removing unwanted columns, usernames, hyperlinks, and null values, followed by converting the textual data to lowercase, which were vectorized using TF-IDF feature extraction to identify significant features. The results of the experiments indicated that the ensemble model outperformed the other models, with a positive class recall of 80% on the Sentiment140 dataset. In [PERSON] et al. (2021) [58], the authors proposed an ensemble model composed of logistic regression, naive Bayes, random forest, XGBoost, and multilayer perceptron to analyze the sentiment of movie reviews. The experiments were performed on the IMDb dataset, which consisted of 25,000 positive and 25,000 negative reviews. The dataset was preprocessed using tokenization and stemming, and by removing URLs, stop words, and punctuation. The feature extraction phase was conducted using the TF-IDF vectorizer. The ensemble model achieved an impressive accuracy of 89.9% on the IMDb dataset. [PERSON] and [PERSON] (2018) [59] tackled the problem of Vietnamese sentiment analysis by implementing an ensemble of machine learning and deep learning models.