# An Emotion-Based Rating System for Books Using Sentiment Analysis and Machine Learning in the Cloud
## 4 Results and Discussion  
The model is then compared against baseline models using several factors. To evaluate the performance of the models, model assessment measures are required [33]. The number of predictions, which were categorized as correct and incorrect, are made using real numbers that are known and can be determined using a confusion matrix. For imparting data fitting based on classes of positive and negative, the confusion matrix shows True Positive (TP), False Negative (FN), False Positive (FP), and True Negative (TN) values. In addition to the F1 score, the model was tested using recall, accuracy, and precision metrics. Figure 8 shows the CNN-LSTM model output obtained after implementation. **Accuracy:** It measures how well the model is capable of performing better across all types of classes. It is particularly estimated when all decisions are equally important and  
Figure 7: Fused attention mechanism based representation of flow of stages in the proposed CNN-LSTM model. helpful. It is determined by dividing the proportion of accurate judgments by the total volume of judgments made. \[ \begin{split} Accuracy=((TP+TN))/((TP+TN+FP+FN))\end{split}  \tag{25}\]  
Table 4 and Figure 9 illustrate the overall effectiveness of various sentiment analysis algorithms applied to multiple datasets. In a comparison of sentiment analysis techniques, CNN-LSTM surpassed all of them, achieving accuracy rates of 95, 94, 96, 95, 94, 96, 95, 94, 96, 95, 94, 96, 95, 94, 93, and 96 percent for each dataset. Figure 8: CNN-LSTM model output. Figure 9: Performance analysis for BOW + IF-IDF with CNN-LSTM based on accuracy. (“Stanford Sentiment...” means “Stanford Sentiment Treebank”). _Precision:_ It determines how accurately the model can classify a sample as positive [34]. It is calculated by dividing the total number of positive cases by the number of positively identified positive samples as correct or incorrect. \[Precision=TP/(TP+FP) \tag{26}\]  
Table 5 and Figure 10 illustrate the overall effectiveness of various sentiment analysis algorithms applied to multiple datasets. Comparing sentiment analysis techniques, CNN-LSTM performs better than all, with precision values of 94, 95, 96, 94, 95, 93, 94, 95, 96, and 95 percent for each dataset. _Recall:_ This score reflects how well the model can recognize positive samples. The percentage is calculated by the ratio of the total volume of positive samples to the total number of positive samples that were correctly recognized as positive. \[Recall=\frac{TP}{(TP+FN)} \tag{27}\]  
Table 6 and Figure 11 display the overall recall performance of several sentiment analysis algorithms applied to various datasets. CNN-LSTM fared better than all other \begin{table}
\begin{tabular}{c c c c c c c} \multicolumn{7}{c}{**Accuracy(\%)**} \\ \multirow{2}{*}{**Database Name**} & **SVM** & **CNN** & **ANN** & **LSTM** & **Bi-LSTM** & **CNN-LSTM** \\ \hline Stanford+ Treebank & 85 & 84 & 86 & 80 & 84 & 95 \\ SemEval Tasks & 84 & 82 & 85 & 81 & 85 & 94 \\ Thai fairy tales & 82 & 81 & 84 & 82 & 86 & 96 \\ SS-Tweet & 83 & 83 & 84 & 84 & 86 & 95 \\ EmoBank & 81 & 84 & 86 & 84 & 84 & 94 \\ ESAR & 82 & 82 & 86 & 82 & 84 & 96 \\ Alm gold & 83 & 83 & 85 & 85 & 86 & 94 \\ EmoTex & 84 & 81 & 86 & 84 & 85 & 95 \\ Text Affect & 82 & 82 & 86 & 82 & 85 & 94 \\ Neviawouskaya & 81 & 84 & 85 & 86 & 84 & 93 \\ Amam’s & 82 & 85 & 84 & 85 & 85 & 96 \\ \multicolumn{7}{c}{} \\ \end{tabular}
\end{table} 
Table 4: Performance analysis for BOW + IF-IDF with CNN-LSTM based on accuracy. Figure 10: Performance analysis for BOW + IF-IDF with CNN-LSTM based on precision. (“Stanford Sentiment...” means “Stanford Sentiment Treebank”). sentiment analysis techniques when compared, with recall rates of 95%, 94%, 96.5%, 92.5%, 94.5%, 95.5%, 96.5%, 94.5%, 91.5%, 94.5%, and 95% for each dataset. _F1-measure:_ The F1-measures are estimated from the harmonic mean of accuracy and recall. \[F1-measure=\frac{(2\times precision\times recall)}{(Precision+recall)} \tag{28}\]