# An Emotion-Based Rating System for Books Using Sentiment Analysis and Machine Learning in the Cloud
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database Name** & **SVM** & **CNN** & **ANN** & **LSTM** & **Bi-LSTM** & **CNN-LSTM** \\ \hline Stanford\(-\)Treebank & 88 & 85 & 86 & 84 & 85 & 95 \\ SemEval Tasks & 85 & 82 & 85 & 82 & 85 & 94 \\ Thai fairy tales & 84 & 84 & 84 & 83 & 87 & 96 \\ SS-Tweet & 86 & 83 & 86 & 84 & 86 & 92 \\ EmoBank & 84 & 82 & 82 & 85 & 84 & 94 \\ SEAR & 85 & 84 & 88 & 84 & 85 & 95 \\ Alm gold & 84 & 85 & 86 & 81 & 86 & 96 \\ EmoTex & 86 & 86 & 87 & 82 & 85 & 94 \\ Text Affect & 85 & 84 & 87 & 83 & 84 & 91 \\ Neviarouskaya & 82 & 85 & 84 & 84 & 86 & 94 \\ Aman’s & 83 & 86 & 85 & 85 & 87 & 95 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance analysis for BOW + IF-IDF with CNN-LSTM based on recall. Figure 11: Performance analysis for BOW + IF-IDF with CNN-LSTM based on recall. (“Stanford Sentiment...” means “Stanford Sentiment Treebank”). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database Name** & \multicolumn{6}{c}{**Precision (\%)**} \\ \cline{2-7}  & **SVM** & **CNN** & **ANN** & **LSTM** & **Bi-LSTM** & **CNN-LSTM** \\ \hline Stanford\(-\)Treebank & 85 & 86 & 85 & 86 & 86 & 94 \\ SemEval Tasks & 86 & 88 & 87 & 87 & 86 & 95 \\ Thai fairy tales & 84 & 84 & 87 & 86 & 84 & 95 \\ SS-Tweet & 89 & 85 & 86 & 87 & 85 & 96 \\ EmoBank & 87 & 86 & 85 & 88 & 87 & 94 \\ SEAR & 88 & 87 & 89 & 84 & 88 & 95 \\ Alm gold & 86 & 82 & 87 & 85 & 85 & 93 \\ EmoTex & 85 & 86 & 88 & 86 & 86 & 94 \\ Text Affect & 85 & 89 & 85 & 87 & 87 & 95 \\ Neviarouskaya & 86 & 87 & 86 & 86 & 85 & 96 \\ Aman’s & 87 & 86 & 87 & 85 & 82 & 95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance analysis for BOW + IF-IDF with CNN-LSTM based on precision. \[F1-Measure=\frac{(2\times TP)}{((2\times TP)+FP+FN)} \tag{29}\]  
Table 7 and Figure 12 display the overall F1-measure performance of several sentiment analysis methods applied to various datasets. In comparing sentiment analysis techniques, CNN-LSTM fared better than all others, with an F1-measure of 94%, 95%, 94%, 93%, 964%, 95%, 93%, 94%, 92%, 91%, and 94% for each dataset. _Sensitivity:_ It measures how well the positive class was expected by referring to the proportion of accurately identified real positives [34]. \[Sensitivity=\frac{TP}{(TP+FN)} \tag{30}\]  
Table 8 and Figure 13 display the total sensitivity of various sentiment analysis approaches applied to multiple datasets. In comparing sentiment analysis techniques, CNN-LSTM fared better than all other approaches, with sensitivity values for each dataset of 94%, 92%, 93%, 91%, 92%, 93%, 91%, 93%, 94%, 94%, and 93%. _Specificity:_ The real negative rate, which is the inverse of sensitivity, is used to assess how accurately the negative class predicted the outcome. An unequal categorization's sensitivity could be more fascinating than its specificity. \[Specificity=\frac{TN}{(FP+TN)} \tag{31}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database Name** & \multicolumn{5}{c}{**F1-Measure**} \\ \cline{2-7}  & **SVM** & **CNN** & **ANN** & **LSTM** & **BiLSTM** & **CNN-LSTM** \\ \hline \hline Stanford—Treebank & 86 & 86 & 87 & 85 & 87 & 94 \\ SemEval Tasks & 85 & 85 & 82 & 84 & 86 & 95 \\ Thai fairy tales & 84 & 84 & 84 & 84 & 85 & 94 \\ SS-Tweet & 86 & 86 & 86 & 85 & 86 & 93 \\ EmoBank & 87 & 85 & 89 & 86 & 88 & 96 \\ ISEAR & 89 & 87 & 87 & 83 & 87 & 95 \\ Alm gold & 85 & 86 & 85 & 86 & 85 & 93 \\ Emoflex & 84 & 85 & 86 & 85 & 84 & 94 \\ Text Affect & 87 & 84 & 84 & 84 & 89 & 92 \\ Neviarouskaya & 88 & 87 & 86 & 85 & 84 & 91 \\ Aman’s & 86 & 86 & 85 & 84 & 85 & 94 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance analysis for BOW + IF-IDF with CNN-LSTM based on F1-measure. Figure 12: Performance analysis for BOW + IF-IDF with CNN-LSTM based on F1-measure. (“Stanford Sentiment...” means “Stanford Sentiment Treebank”). Table 9 illustrates the overall effectiveness of various sentiment analysis algorithms applied to multiple datasets.