# RoBERTa-GRU: A Hybrid Deep Learning Model for Enhanced Sentiment Analysis
## 2 Related Works
### Machine Learning Approaches  
[PERSON] and [PERSON] (2018) [3] conducted sentiment analysis on a dataset collected from Indian Airlines using seven classical machine learning algorithms, namely Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, and AdaBoost. The dataset was preprocessed to remove stop words and perform lemmatization. The results showed that the AdaBoost model achieved the highest precision of 84.5%.  
[PERSON] et al. (2019) [4] performed sentiment analysis on a dataset of tweets regarding homosevuality in Indonesia using Support Vector Machines (SVM). The dataset was labeled into two classes: positive and negative. The raw data were preprocessed to remove stop words and perform lemmatization and stemming. The Term Frequency-Inverse Document Frequency (TF-IDF) was used as the feature representation for the SVM. The method achieved an accuracy of 99.5% on the dataset.  
In the research carried out by [PERSON] (2020) [5], Multinomial Naive Bayes was used for sentiment analysis on Arabic tweets. The authors employed a 4-g tokenization technique and Khoja stemmer for text preprocessing, and represented the processed text as TF-IDF features. The Multinomial Naive Bayes model was trained on the dataset containing 2000 tweets, which were labeled into positive and negative classes and evaluated using five-fold cross validation. The proposed approach demonstrated an impressive accuracy of 87.5% on the Arabic tweet dataset.  
In the study by [PERSON] et al. (2018) [6], various classification algorithms were compared for sentiment analysis on product review tweets. The methods explored included Linear Discriminant Analysis, K-Nearest Neighbors, Classification Furthermore, Regression Trees (CART), SVM, Random Forest, and C5.0. The dataset comprised 1150 tweets that underwent preprocessing steps, including stop words and punctuation removal, case folding, and stemming. The cleaned text was transformed into a Term Document matrix and fed into the classification algorithms for sentiment analysis. The experimental results indicated that the CART method achieved the highest accuracy of 88.99%.  
[PERSON] et al. (2019) [7] proposed a sentiment analysis approach that leverages four different machine learning algorithms: Logistic Regression, Decision Tree, Support Vector Machine, and Neural Network. The sentiment140 dataset was used in the experiments. The raw data were preprocessed, including stop-words removal and lemmatization, and then represented as TF-IDF features. The authors found that the Neural Network model achieved the highest accuracy of 80% compared with the other models.  
Similarly, [PERSON] et al. (2021) [8] developed a sentiment analyzer that employs a set of machine learning algorithms, including Naive Bayes, Bernoulli Naive Bayes, Multinomial Naive Bayes, Logistic Regression, and Linear Support Vector Classification. The experiments were conducted using the \"twitter samples\" corpus in the Natural Language Toolkit, which includes 5 k positive and 5 k negative tweets. The preprocessing steps included tokenization, stop-words removal, URLs removal, symbols removal, case folding, and lemmatization. The results showed that the Naive Bayes model achieved the highest accuracy of 99.73%.  
This subsection discusses different studies that use various machine learning algorithms for sentiment analysis on different datasets, including Indian Airlines feedback, Indonesian tweets about homosexuality, Arabic tweets, product review tweets, and the sentiment140 dataset. Preprocessing steps, such as stop-words removal, lemmatization, and stemming, were employed in the majority of the studies. The algorithms used include AdaBoost, Support Vector Machine, Multinomial Naive Bayes, Logistic Regression, Decision Tree, Bernoulli Naive Bayes, and Linear Support Vector Classification. The results show that the accuracy of the algorithms varies depending on the dataset and the algorithm used, with accuracy ranging from 80% to 99.73%.