# Enhancing Imbalanced Sentiment Analysis: A GPT-3-Based Sentence-by-Sentence Generation Approach
## 5 Results
### Sentiment Classification
#### 5.2.2 Sentiment Classification Results  
The sentiment classification results, featuring balanced accuracy and macro F1-score as metrics, are summarized in Table 6. Meanwhile, Figure 8 visually displays the improvements in the accuracy and F1-score of the proposed datasets over the original dataset. These proposed datasets encompass those created through both sentence-by-sentence and fine-tuning techniques. The objective is to evaluate the classification performance of the sentence-by-sentence generated datasets in comparison to the fine-tuning dataset, especially regarding their improvements over the original imbalanced dataset. The datasets generated via the sentence-by-sentence generation technique--namely, Dataset-100, Dataset-80, Dataset-75, and Dataset-50--demonstrate significant improvements in sentiment classification performance across all models, reflected in increased \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**RNN (GloVe)**} & \multicolumn{2}{c}{**CNN (GloVe)**} & \multicolumn{2}{c}{**LSTM (GloVe)**} & \multicolumn{2}{c}{**BiLSTM (GloVe)**} & \multicolumn{2}{c}{**GRU (GloVe)**} \\  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline Dataset-100 & 60.10\% & 56.61\% & 60.77\% & 60.18\% & 60.64\% & 60.38\% & 63.54\% & 60.73\% & 62.18\% & 60.13\% \\ Dataset-80 & 58.40\% & 57.97\% & 61.53\% & 59.80\% & 60.72\% & 60.14\% & 60.45\% & 59.77\% & 61.16\% & 60.28\% \\ Dataset-75 & 58.48\% & 57.37\% & 59.64\% & 60.13\% & 64.08\% & 61.65\% & 61.16\% & 60.00\% & 63.24\% & 62.24\% \\ Dataset-50 & 58.16\% & 58.84\% & 59.90\% & 59.42\% & 63.00\% & 61.27\% & 60.76\% & 59.99\% & 60.18\% & 60.94\% \\ Fine-tuning Dataset & 64.71\% & 62.80\% & 71.46\% & 65.66\% & 66.76\% & 64.54\% & 66.44\% & 66.03\% & 67.49\% & 66.67\% \\ Original Dataset & 33.33\% & 30.49\% & 54.92\% & 54.31\% & 54.33\% & 54.50\% & 53.62\% & 54.40\% & 55.90\% & 54.44\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sentiment classification result. Figure 8: Improvement in accuracy and F1-score of proposed datasets compared to original dataset. accuracy and F1-score metrics. This underscores the efficacy of our synthetic data in enhancing classification performance. Notably, Dataset-100, which represents a perfectly balanced dataset, shows a marked improvement in model accuracy and F1-score across all models when compared to the original dataset. The Recurrent Neural Network (RNN) with GloVe embeddings, for instance, shows an increase in accuracy from 33.33% in the original dataset to 60.10% in Dataset-100, with a similar improvement in the F1-score. This trend is consistent across other models, such as the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), confirming the benefit of a balanced dataset in sentiment classification tasks. Nevertheless, when compared to the fine-tuning dataset, the sentence-by-sentence datasets exhibit slightly lower performance. The fine-tuning dataset stands out with the highest accuracy and F1-score for all models. For example, the fine-tuning dataset improves the accuracy of the GRU model to 67.49% and the F1-score to 66.67%, outperforming all sentence-by-sentence datasets. Indeed, for all models, the fine-tuning dataset successfully delivers performance improvements of over 10%. Specifically, for the RNN model, this fine-tuning dataset can achieve performance increases of over 30% in both the accuracy and F1-score. Delving further into data quality and balance, it becomes apparent that Dataset-100, which is fully balanced, typically yields better performance enhancements than the other sentence-by-sentence-generated datasets.