## 2 Literature Review  
Sentiment analysis has been applied across various economic sectors, particularly in tourism, where analyzing client reviews of hotel experiences helps make decisions to enhance service quality. Developing effective predictive models for sentiment analysis requires a comprehensive examination of each stage in the process. Feature extraction and artificial intelligence algorithms are important steps that significantly impact the accuracy of the final predictive results. Numerous studies, as summarized in Table 1, rely on a single feature extraction method, such as TF-IDF or Word2 Vec, often applied to one or two datasets, and trained with a limited set of machine learning or deep learning algorithms. However, there has been little exploration of the performance of these feature extraction techniques when combined with various machine learning, deep learning, and transformer models across multiple datasets in different languages. In ref. [9], the authors analyze the performance of various machine learning algorithms for sentiment analysis, focusing on an English language dataset labeled into two categories: positive and negative opinions on news articles. The best model achieves an accuracy of 98.02% by using traditional TF-IDF feature extraction combined with the naive Bayes (NB) algorithm. In another study [10], researchers compare three feature extraction techniques: bag of words (BOW), TF-IDF, and Word2 Vec across several machine learning algorithms. The re \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Dataset** & **Resampling Techniques** & **Feature Extraction** & **Classifiers** & **Results** \\ \hline
[9] & A dataset of English news articles & No & TF-IDF & NB, logistic regression (LR), linear SVC, gradient boosting & Best model = NB Accuracy = 89.02\% \\
[10] & A dataset of English tweets related to COVID-19 & No & Bag of words (BoW), TF-IDF, Word2 Vec & RB, multinomial NB, SVC & Best model = TF-IDF with SVM Accuracy = 71\% \\  & Six English datasets combined from Kaggle & No & Word2 Vec, BERT & CNN, RNN, BiLSTM & Best model = BERT with CNN, RNN and BiLSTM Accuracy = 93\% \\  & A combination of six English datasets from Kaggle & No & BoW, TF-IDF, AraVec & LR, RF, Extra Trees (ETrees), linear SVM, SVM RBF & Best model = TF-IDF with LR Accuracy = 82.10\% \\  & Arabic dataset of Twitter comments & No & Word2 Vec, Glove, FastText & Gaussian NB, linear SVC, NuSVC, LR, SGD, RF & Best model = FastText with NuSVC Accuracy = 84.89\% \\  & An Arabic dataset of user opinions on a mobile application & No & Supervised lexicon weights, TDM, TFM & Best model = hybrid (EC) & Best model = TF-IDF with SVM, ensemble classifier (EC) & Best model = TF-IDF with Accuracy = 89.2\% \\  & Arabic datasets of hotel reviews & No & TF-IDF & KNN, LSTM & Precision metric between 73\% and 77\% \\  & (Datasets A and C) & No & TF-IDF & Adaboost, KNN, DT, maximum entropy (ME), SVM, ensemble classifier (EC) & Best model = TF-IDF with EC Accuracy = 89.2\% \\  & A dataset of customer reviews from a French e-commerce platform & No & TF-IDF & RNN, LSTM & Precision metric between 73\% and 77\% \\  & SemEval2016 French dataset, including restaurant and museum reviews & No & BERT’s variants & Conventional methods like LSTM Fine-tuned models using FC, SPC, and AEN approaches & Best model = FlauBERT with a pretrained fully connected model (PTM-FC) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of related works: approaches and results. sults show that TF-IDF with support vector machine (SVM) is more effective than the other techniques, achieving an accuracy of 71%, compared to 56% for Word2 Vec and SVM. These techniques were applied to an English language Twitter dataset related to COVID-19. In ref. [11], the authors classify sentiment analysis using six combined English datasets, leveraging bidirectional encoder representations from transformers (BERT) like an embedding technique and comparing it to another embedding technique, Word2 Vec.