# RoBERTa-GRU: A Hybrid Deep Learning Model for Enhanced Sentiment Analysis
## 6 Experimental Results and Analysis
The Sentiment140 dataset consists of relatively short tweets, making GRUs a fitting choice for this particular task. Table 9 presents a comparison of various sentiment analysis models on the Twitter US Airline dataset. The results indicate that the proposed RoBERTa-GRU model outperforms all the other models, with an accuracy of 91.52% and an F1-score of 91%, which represents an improvement of 11.05% in accuracy and 19.52% in F1-score compared with the logistic regression model that records an accuracy of 80.5% and an F1-score of 72%. The RoBERTa-GRU model uses the RoBERTa Transformer to capture contextual information from the text, and the GRU layer to model the sequential information. This combination of a Transformer and a recurrent layer results in better performance than models that only use one of these layers, such as the RoBERTa-LSTM. In addition to that, the proposed RoBERTa-GRU model benefits from data augmentation using the GloVe word embedding technique, which helps to mitigate the imbalanced dataset problem and synthesize more samples for better model learning. This data augmentation technique enhances the performance of the model, resulting in an improved accuracy of 91.52%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Naive Bayes [8] & 87.01 & 87 & 87 & 87 \\ Logistic Regression [23] & 87.12 & 90 & 90 & 90 \\ Decision Tree [24] & 73.46 & 74 & 73 & 73 \\ KNN [23] & 77.37 & 78 & 77 & 77 \\ AdaBoost [24] & 83.37 & 83 & 83 & 83 \\ GRU [25] & 87.88 & 88 & 88 & 88 \\ LSTM [25] & 85.11 & 85 & 85 & 85 \\ BiLSTM [26] & 86.28 & 87 & 86 & 86 \\ CNN-LSTM [12] & 86.07 & 86 & 86 & 86 \\ CNN-BiLSTM [12] & 86.16 & 86 & 86 & 86 \\
**RoBERTa-GRU** & **94.63** & **95** & **95** & **95** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The comparison results on the IMDb. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Naive Bayes [8] & 76.57 & 77 & 77 & 77 \\ Logistic Regression [23] & 78.01 & 78 & 78 & 78 \\ Decision Tree [24] & 62.34 & 69 & 62 & 59 \\ KNN [23] & 60.39 & 66 & 60 & 57 \\ AdaBoost [24] & 69.94 & 71 & 70 & 69 \\ GRU [25] & 78.96 & 78 & 78 & 78 \\ LSTM [25] & 79.10 & 79 & 79 & 79 \\ BiLSTM [26] & 78.53 & 78 & 78 & 78 \\ CNN-LSTM [12] & 77.53 & 77 & 77 & 77 \\ CNN-BiLSTM [12] & 77.58 & 77 & 77 & 77 \\
**RoBERTa-GRU** & **89.59** & **90** & **90** & **90** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The comparison results on the Sentiment140 dataset. Overall, the proposed RoBERTa-GRU model outshines other methods on three datasets, reflecting its superior performance in sentiment analysis. The pretrained RoBERTa model has been trained on an enormous corpus of text data, enabling it to learn complex language patterns and relationships between words and phrases. Additionally, the RoBERTa's dynamic masking patterns improve its ability to generalize and adapt to new text sequences. The gating mechanism of a GRU addresses the vanishing gradients issue by selectively updating its hidden state, allowing it to better capture the long-range dependencies in text.