# Enhancing Product Design through AI-Driven Sentiment Analysis of Amazon Reviews Using BERT
## 4 Results and Discussion  
The BERT-based classification model is pre-trained comparatively on a low amount of data. Hence, to train the model on three datasets, the training and validation experiments run on the Google collab using the T4 GPU. Once the tokenizer and sequence classification models are trained, these models catch 386 MB, and both are saved in the drive to recall and deploy them in the NLP prediction pipeline to predict the aspects of the input review data. T5-small and T5-base both are deployed and fine tuned on the Google collab using T4 GPU. **Accuracy**: the proportion of correctly classified reviews among the total reviews. **Precision**: the accuracy of positive predictions, considering both true positives and false positives. **Recall**: the proportion of correctly predicted positive reviews out of all actual positives. **F1 Score**: the means of precision and recall, providing a balanced measure. To evaluate the performance of a large language model, we required calculate the accuracy, precision, recall, and F-score. The evaluation metrics for the BERT model on COMB dataset are resulted in Table 4 and the evaluation metric results for T5-Base and T5-small can be seen in Tables 5 and 6. In the below tables, the accuracy for the three models is compared and can be seen as 92% for BERT, 91% for T5-base, and 88%- T5-small. Three models show almost equal results in terms of accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Adaptability & 0.94 & 0.89 & 0.92 & 142 \\ \hline Aesthetics & 0.92 & 0.94 & 0.93 & 130 \\ \hline Durability & 0.88 & 0.92 & 0.90 & 183 \\ \hline Ease of Reprocessing & 0.93 & 0.95 & 0.94 & 121 \\ \hline Ease of Storage & 0.97 & 0.95 & 0.96 & 125 \\ \hline Ease of Use & 0.93 & 0.89 & 0.91 & 133 \\ \hline Ergonomics & 0.90 & 0.86 & 0.88 & 139 \\ \hline Interference & 0.88 & 0.92 & 0.90 & 130 \\ \hline Performance & 0.85 & 0.85 & 0.85 & 231 \\ \hline Price & 1.00 & 0.95 & 0.97 & 128 \\ \hline Safety & 0.90 & 0.96 & 0.93 & 125 \\ \hline Use Efficiency & 0.97 & 0.95 & 0.96 & 146 \\ \hline Accuracy & & & 0.92 & 1733 \\ \hline Macro Avg & 0.92 & 0.92 & 0.92 & 1733 \\ \hline Weighted Avg & 0.92 & 0.92 & 0.92 & 1733 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation metrics for the BERT model trained on COMB dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predicted Aspect** & **Sentiment Label** & **Sentiment Score** & **Common Words** \\ \hline Durability & Negative & \(-0.5423\) & like, bought, march \\ \hline Adaptability & Neutral & 0 & used, bread, bake \\ \hline Ease of Use & Positive & 0.6597 & like, easy, use \\ \hline Price & Positive & 0.9442 & toaster, oven, make \\ \hline Performance & Negative & \(-0.2091\) & control, not work \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._In Figure 3, evaluation metrics of the three aspect detection models are compared. BERT model comparatively efficient and performs better than T5-Base and small. In this paper in the prediction pipeline, we utilized the fine-tuned BERT model as the final NLP model to detect the aspect for sentiment analysis. According to the results, BERT outperformed the T5 model. But this is not the only reason to choose BERT over T5. In the training stage, T5 models, excluding the 12 aspect labels they generated as new labels