# TAWC: Text Augmentation with Word Contributions for Imbalance Aspect-Based Sentiment Classification
## 5 Results and Discussion
### Ablation Study  
We designed the TAWC technique for selective augmentation, which is determined by using two metrics, (1) analytical correlation and (2) semantic similarity, to measure the relationship between a word and a sentiment polarity. In the ablation study, correlation and similarity measurements were applied separately to small training data to investigate which approach greatly influenced keyword extraction. We designed two types of TAWCs:  
1. **TAWC-C**: We only applied correlation to extract words in the sentence into two groups: high and low correlation.
2. **TAWC-S**: We only used semantic similarity to separate words in the text into two types: high and low similarity.  
We ran experiments on 200 training sets with the balance class, while the test data remained unchanged. The experimental results are illustrated in Figure 5. Both TAWC-C and TAWC-S outperformed the training data without augmentation and EDA over the three classifiers on the two datasets, indicating selective words by correlation and semantic similarity function. In addition, each approach led to improvement, and the integration of the two approaches showed the best performance; thus, they were complementary.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**SemEval 2015**} & \multicolumn{3}{c}{**SemEval 2016**} \\ \hline
**Models** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline
**Methods** & **Avg \(\pm\) Std** & **Avg \(\pm\) Std** & **Avg \(\pm\) Std** & **Avg \(\pm\) Std** & **Avg \(\pm\) Std** \\  & **(Best)** & **(Best)** & **(Best)** & **(Best)** & **(Best)** & **(Best)** \\ \hline No-DA & 80.29 \(\pm\) 0.41 & 78.22 \(\pm\) 0.12 & 81.48 \(\pm\) 0.44 & 83.91 \(\pm\) 0.57 & 85.58 \(\pm\) 0.12 & 85.60 \(\pm\) 0.33 \\  & (80.72) & (78.39) & (81.94) & (84.49) & (85.76) & (85.95) \\ SR & 81.26 \(\pm\) 0.48 & 81.13 \(\pm\) 0.77 & 83.58 \(\pm\) 0.48 & 88.97 \(\pm\) 0.85 & 87.10 \(\pm\) 0.13 & 89.67 \(\pm\) 0.61 \\  & (81.78) & (81.90) & (83.92) & (90.15) & (87.23) & (90.37) \\ SI & 80.11 \(\pm\) 0.10 & 81.14 \(\pm\) 0.35 & 83.76 \(\pm\) 0.17 & 89.87 \(\pm\) 0.45 & 87.12 \(\pm\) 0.53 & 90.03 \(\pm\) 0.47 \\  & (80.23) & (81.50) & (83.84) & (90.32) & (87.69) & (90.58) \\ SD & 80.25 \(\pm\) 0.96 & 80.03 \(\pm\) 0.65 & 83.58 \(\pm\) 0.48 & 90.12 \(\pm\) 0.39 & 86.03 \(\pm\) 0.12 & 89.08 \(\pm\) 0.70 \\  & (81.28) & (80.69) & (83.92) & (90.15) & (86.15) & (89.85) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of performance for each selective augmentation procedure.  
However, according to the boxplots, removing the analytical correlation or semantic similarity led to decreased accuracy. The TAWC-S score was worse than the TAWC-C score, suggesting that the statistical correlation factor is more crucial than semantic similarity for the TAWC procedure. This finding has inspired us to explore similarity measurements further in future works. However, compared to the original TAWC, the results obtained by TAWC-C and TAWC-S demonstrate that word selection is a promising method for text augmentation.