# 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
In contrast, outputs with high scores in novelty, diversity, and cosine similarity are considered to meet the criteria for extensive diversity, significant novelty, and coherence. On the flip side, very low novelty and diversity scores suggest that the generated outputs are highly similar to other outputs and even to the input. This evaluation process is critical to ascertaining whether the good-quality synthetic data produced by sentence-by-sentence generation meet our intended standards. Given the challenges encountered in acquiring high-quality synthetic data--specifically, the issues of duplication or high similarity among outputs--we were prompted to explore various outcomes by setting different cut-off thresholds as a means to manage similarity. Consequently,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sentiment**} & \multirow{2}{*}{**Cut-Off**} & \multicolumn{1}{c}{**Similar**} & \multirow{2}{*}{**Similar to**} & \multirow{2}{*}{**Anomaly**} & \multirow{2}{*}{**Bad**} & \multirow{2}{*}{**Good**} & \multicolumn{2}{c}{**Total**} & \multirow{2}{*}{**\% Bad**} \\  & & & & & & & & **Output** \\ \hline \multirow{4}{*}{Negative} & 1 & 571 & 0 & 1 & 572 & 15,349 & 15,921 & 3.59\% \\  & 0.80 & 1558 & 99 & 1 & 1658 & 14,263 & 15,921 & 10.41\% \\  & 0.75 & 1889 & 96 & 1 & 1986 & 13,935 & 15,921 & 12.47\% \\  & 0.50 & 5147 & 74 & 1 & 5222 & 10,699 & 15,921 &we established four distinct sets of good-quality synthetic data, each corresponding to different cut-off levels. Figure 6 presents the evaluation results for these datasets, show-casing the implications of our findings for the novelty, diversity, similarity, and cosine similarity metrics. A novelty score close to 0 represents similarity between an output and all inputs, meaning that the similarity could be between an output and its corresponding input or other inputs. Figure 6 shows that for both negative and neutral data, there are no longer any novelty values very close to 0 across all cut-off datasets, meaning there is no longer a high similarity between the output and input. This is also confirmed by the similarity score metric, which represents the similarity between the output and its input. Across all cut-offs, for both negative and neutral sentiments, there are no values close to 1, indicating that no outputs have a high similarity to their inputs. Then, a diversity score close to zero reflects similarity among outputs. The histogram clearly shows that for both negative and neutral sentiment data, outputs from a cut-off of 1 still have relatively many diversity scores close to 0 compared to outputs from other cut-offs. This suggests that simply removing exact duplicates is not sufficient, meaning there are still outputs with high similarity to other outputs. For cut-offs of 0.8 and 0.75, there are very few data points where the diversity score is still close to 0, indicating that our similarity detection algorithm still has weaknesses. For instance, because the process begins by sorting outputs, if there are outputs that use the same words as others but in a different order, they will not be detected as similar pairs. Furthermore, in terms of anomalies, the novelty and diversity scores show no values very close to 1, meaning there are no outputs that are entirely different in characteristics from the rest. This is also confirmed by the cosine similarity value, which serves as a metric to measure the good-quality synthetic data in this study, namely, the coherence between outputs and inputs. The histogram shows that for both negative and neutral data, across all cut-offs, the cosine similarity scores are above 0.6, meaning the generated outputs have a high semantic similarity to their inputs based on GloVe embeddings. Therefore, based on these results, we can deduce that, in general, the good-quality synthetic data obtained generally meet the standards we set. However, there is still a minor issue that poses a problem: similarity.