# Enhancing Imbalanced Sentiment Analysis: A GPT-3-Based Sentence-by-Sentence Generation Approach
## 5 Results
### Sentence-by-Sentence Generation
#### 5.1.2 Evaluation of Generated Synthetic Data
The explanation is as follows:  
* Cut-off =1 A cut-off of 1 is referred to as an exact duplication, which is identified using the \"duplicated\" function in the pandas library. From the overall duplication, one entry is selected to be retained in the dataset of unique outputs. A total of 571 data points were removed due to being duplicates of other outputs. After removing duplicates, no exact duplications between outputs and inputs were detected. There was only one data anomaly for negative sentiment, characterized by a novelty value of 0.98. Conversely, no data anomalies were found in the neutral sentiment data. From this process, a total of 15,349 outputs of good-quality synthetic data for negative sentiment were obtained. * Cut-off = 0.8 Contrasting with a cut-off of 1, good-quality output with a cut-off of 0.8 was achieved by discarding outputs that have a similarity of 80% or higher to other outputs or \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sentiment**} & \multirow{2}{*}{
\begin{tabular}{c} **The Number** \\ **of Replications** \\ \end{tabular} } & \multicolumn{4}{c}{**Frequency**} \\ \cline{3-6}  & & **Unique** & **Overall** & **Intra-** & **Inter-** \\  & **Outputs** & **Duplication** & **Duplication** & **Duplication** \\ \hline Negative & 9 & 15,058 & 863 & 767 & 311 \\ Neutral & 17 & 14,029 & 1067 & 1042 & 124 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Duplication analysis among outputs. Figure 5: Novelty and diversity scores of all generated synthetic data. to the input, as well as removing anomalous data. A total of 1558 data points were discarded due to similarities to other outputs, and 99 data points were removed due to similarities to the input. By applying a cut-off of 0.8, a total of 14,263 good-quality synthetic data outputs for negative sentiment were obtained. * Cut-off = 0.75 Following the same process as with a cut-off of 0.8, using a cut-off of 0.75 resulted in 13,935 outputs of good-quality synthetic data for negative sentiment, while 1986 outputs were of bad quality. * Cut-off = 0.5 With a cut-off of 50, a total of 10,699 good-quality synthetic data outputs were produced, along with 5222 bad-quality outputs. Furthermore, from Table 5, we can see that as the cut-off value is lowered, the proportion of data classified as \"bad\" automatically increases, meaning that the amount of good-quality synthetic data available for use diminishes. This trend leads to a shortfall in the quantity of good-quality synthetic data necessary for achieving a balance within the dataset. For negative sentiment, the number of good-quality synthetic data derived from a cut-off of 0.5 falls short at only 10,699 outputs, whereas 12,441 are required to establish a balance. Similarly, for neutral sentiment, the good-quality synthetic data generated with cut-offs of 0.8, 0.75, and 0.5 are insufficient to meet the balancing need of 13,322 data points. Therefore, only the dataset balanced at a cut-off of 1 can yield a fully balanced dataset. In contrast, datasets derived from other cut-off values do not achieve total balance, indicating the critical impact of the chosen cut-off threshold on data quality and utility. (4) Evaluation of good-quality synthetic data  
In this study, we define good-quality synthetic data as data that exhibit extensive diversity, demonstrate significant novelty, and maintain coherence with the original dataset concerning course reviews. The approach to assessing this involves analyzing novelty and diversity scores, where values approaching 1 could signal anomalous data, while scores close to 0 might suggest high similarity within outputs and to the input. The use of cosine similarity metrics is thus essential to ascertain the coherence between outputs and inputs. High novelty and diversity scores paired with low cosine similarity would indicate outputs that are not coherent with the inputs.