# Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis
## 4 Experimental Results
### Recommendation System  
For the recommendation model, we tested item-based and user-based CF techniques using cosine similarity. To verify the effectiveness of our proposal, we contrasted the performance of conventional memory-based CF techniques with the same techniques enhanced by our proposal. Tables 6-8 and Figures 10-12 present the findings of the MAE and RMSE measures for rating prediction on the Amazon Kindle books and Amazon digital music datasets. They  
Figure 8: Performance Metrics. Figure 9: Performance metrics of the second scenario. were calculated based on the memory-based (item-based and user-based) methods before and after using sentiment analysis based on Bi-LSTM sentiment model. The parameter \(\alpha\) is employed to control the significance of the recommendation results with and without sentiment in Equation (8). * Experience 1: User-Based results \begin{table}
\begin{tabular}{l l l} \hline  & **MAE** & **RMSE** \\ \hline Without SA & 2.30 & 2.63 \\ \hline With SA (\(\alpha=0.3\)) & 1.16 & 1.34 \\ \hline With SA (\(\alpha=0.5\)) & 1.13 & 1.31 \\ \hline With SA (\(\alpha=0.7\)) & 1.12 & 1.30 \\ \hline \end{tabular}
\end{table} 
Table 6: Evaluation metrics values obtained for user-based CF technique using the kindle book reviews dataset. \begin{table}
\begin{tabular}{l l l} \hline  & **MAE** & **RMSE** \\ \hline Without SA & 2.18 & 2.66 \\ \hline With SA (\(\alpha=0.3\)) & 1.33 & 1.39 \\ \hline With SA (\(\alpha=0.5\)) & 1.17 & 1.31 \\ \hline With SA (\(\alpha=0.7\)) & 1.15 & 1.28 \\ \hline \end{tabular}
\end{table} 
Table 7: Evaluation metrics values obtained for User-based CF technique using Amazon digital music dataset. Figure 10: Evaluation metrics values obtained for user-based CF technique using Kindle book reviews dataset. The results from the user-based collaborative filtering technique, presented in Tables 6 and 7, as well as Figures 10 and 11, provide insights into the performance of the recommendation system using the Kindle book reviews dataset and the Amazon digital music dataset. For the Kindle book reviews dataset, when sentiment analysis was incorporated with different \(\alpha\) values, notable improvements in evaluation metrics were observed compared to the approach without SA. The MAE decreased from 2.30 (without SA) to 1.12 (with SA and \(\alpha\) = 0.7), while the RMSE decreased from 2.63 to 1.30. This indicates that incorporating sentiment analysis, particularly with \(\alpha\) = 0.7, resulted in more accurate predictions and reduced errors. Similar trends were observed for the Amazon digital music dataset. The inclusion of SA with different \(\alpha\) values led to a reduction in MAE and RMSE. The lowest values were achieved with SA and \(\alpha\) = 0.7, with MAE of 1.15 and RMSE of 1.28, compared to MAE of 2.18 and RMSE of 2.66 without SA. Overall, these results demonstrate the effectiveness of incorporating sentiment analysis into the user-based CF technique. * Experience 2: Item-Based results \begin{table}
\begin{tabular}{l l l} \hline  & **MAE** & **RMSE** \\ \hline Without SA & 2.36 & 2.73 \\ \hline With SA (\(\alpha\) = 0.3) & 1.18 & 1.35 \\ \hline With SA (\(\alpha\) = 0.5) & 1.25 & 1.45 \\ \hline With SA (\(\alpha\) = 0.7) & 1.54 & 1.80 \\ \hline \end{tabular}
\end{table} 
Table 8: Evaluation metrics values obtained for item-based CF technique using Kindle book review dataset. Figure 11: Evaluation metrics values obtained for user-based CF technique using Amazon digital music dataset. The results from the item-based collaborative filtering technique, as presented in Tables 8 and 9 and Figures 12 and 13, provide insights into the performance of the recommendation system using the Kindle book review dataset and the Amazon digital music dataset. For the Kindle book review dataset, incorporating sentiment analysis with different \(\alpha\) values resulted in varying effects on the evaluation metrics. While using SA with \(\alpha\) = 0.3 led to slight improvements in MAE (1.18) and RMSE (1.35) compared to the approach without SA (MAE: 2.36, RMSE: 2.73), \(\alpha\