# Enhancing Product Design through AI-Driven Sentiment Analysis of Amazon Reviews Using BERT
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Adaptability & 0.82 & 0.89 & 0.86 & 142 \\ \hline Aesthetics & 0.82 & 0.78 & 0.80 & 130 \\ \hline Durability & 0.86 & 0.93 & 0.89 & 183 \\ \hline Ease of Reprocessing & 0.86 & 0.93 & 0.89 & 121 \\ \hline Ease of Storage & 0.92 & 0.94 & 0.93 & 125 \\ \hline Ease of Use & 0.91 & 0.87 & 0.89 & 133 \\ \hline Ergonomics & 0.82 & 0.80 & 0.81 & 139 \\ \hline Interference & 0.84 & 0.92 & 0.88 & 130 \\ \hline Performance & 0.90 & 0.78 & 0.84 & 231 \\ \hline Price & 0.98 & 0.95 & 0.96 & 128 \\ \hline Safety & 0.97 & 0.90 & 0.93 & 125 \\ \hline Use Efficiency & 0.93 & 0.96 & 0.95 & 146 \\ \hline \hline Accuracy & & & 0.88 & 1733 \\ \hline Macro Avg & 0.89 & 0.89 & 0.89 & 1733 \\ \hline Weighted Avg & 0.88 & 0.88 & 0.88 & 1733 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation metrics for the T5-small model trained on COMB dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Adaptability & 0.89 & 0.89 & 0.89 & 142 \\ \hline Aesthetics & 0.87 & 0.95 & 0.91 & 130 \\ \hline Durability & 0.97 & 0.93 & 0.95 & 183 \\ \hline Ease of Reprocessing & 0.94 & 0.93 & 0.93 & 121 \\ \hline Ease of Storage & 0.94 & 0.95 & 0.95 & 125 \\ \hline Ease of Use & 0.89 & 0.89 & 0.89 & 133 \\ \hline Ergonomics & 0.91 & 0.80 & 0.85 & 139 \\ \hline Interference & 0.90 & 0.94 & 0.92 & 130 \\ \hline Performance & 0.85 & 0.86 & 0.86 & 231 \\ \hline Price & 0.95 & 0.97 & 0.96 & 128 \\ \hline Safety & 0.91 & 0.97 & 0.94 & 125 \\ \hline Use Efficiency & 0.96 & 0.92 & 0.94 & 146 \\ \hline Accuracy & & & 0.91 & 1733 \\ \hline Macro Avg & 0.92 & 0.92 & 0.92 & 1733 \\ \hline Weighted Avg & 0.91 & 0.91 & 0.91 & 1733 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation metrics for the T5-base model trained on COMB dataset. in Table 7, we can see duplicate aspects such as attraction, color, charge. To fix this, we used cosine similarity to map the duplicate labels to original labels. Sentence transformer, a library, is used to map these labels to original labels. This affects the computational efficiency of the T5 model, and the BERT model obtained an advantage over these models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Adaptability & 0.89 & 0.89 & 0.89 & 142 \\ \hline Aesthetic & 0.00 & 0.00 & 0.00 & 0 \\ \hline Aesthetics & 0.87 & 0.92 & 0.90 & 130 \\ \hline Attraction & 0.00 & 0.00 & 0.00 & 0 \\ \hline Charge & 0.00 & 0.00 & 0.00 & 0 \\ \hline Colors & 0.00 & 0.00 & 0.00 & 0 \\ \hline Durability & 0.97 & 0.93 & 0.95 & 183 \\ \hline Ergonomics & 0.91 & 0.80 & 0.85 & 139 \\ \hline Integration & 0.00 & 0.00 & 0.00 & 0 \\ \hline Interference & 0.90 & 0.94 & 0.92 & 130 \\ \hline Maintenance & 0.00 & 0.00 & 0.00 & 0 \\ \hline Performance & 0.85 & 0.86 & 0.86 & 231 \\ \hline Price & 0.95 & 0.97 & 0.96 & 128 \\ \hline Reprocessability & 0.94 & 0.93 & 0.93 & 121 \\ \hline Safety & 0.91 & 0.97 & 0.94 & 125 \\ \hline Storability & 0.94 & 0.95 & 0.95 & 125 \\ \hline Usability & 0.89 & 0.89 & 0.89 & 133 \\ \hline Use Efficiency & 0.96 & 0.92 & 0.94 & 146 \\ \hline \hline Accuracy & & & 0.91 & 1733 \\ \hline Macro avg & 0.61 & 0.61 & 0.61 & 1733 \\ \hline Weighted avg & 0.91 & 0.91 & 0.91 & 1733 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: T5-base model generating duplicate aspects. Figure 3: Comparison of evaluation metrics for three models on COMB dataset. The aspects are predicted, and the distribution of the aspects in the training dataset can be seen in Figure 4.  
The distribution of the customer ratings in the input data can be seen in Figure 5. Most portions are occupied with five- and 4-star ratings, and the other rating distribution is varied.