## 4 Results  
This section presents a comparison of several algorithms for the sentiment classification of textual data. All the algorithms were presented in Section 3. The performance of these algorithms was analyzed on the provided data-set, which contains three classes (_negative, neutral, and positive_). The aim was to evaluate how well the algorithms can classify tweets into one of these three classes. The used evaluation metrics [80] were precision, recall, and \(F_{1}\) score and accuracy score [81] as performance measures. We also used the hamming loss metric [82] to assess how well the algorithms can handle multi-class classification. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**ID\_Model** & **Short ID** & **[cl \({}^{a}\)]** & **[f \({}^{b}\)]** & **[fs \({}^{c}\)]** & **[d \({}^{d}\)]** & **[e \({}^{e}\)]** & **[b / f]** \\ \hline \multicolumn{8}{c}{CNN} \\ \hline cmn\_cl1\_64-f85 d5 e10 b16 & CNN1 & 1 & 64 & 5 & 0.5 & 10 & 16 \\ cmn\_cl1\_64-f85 d5 e10 b32 & CNN1b32 & 1 & 64 & 5 & 0.5 & 10 & 32 \\ cmn\_cl1\_64-f85 d5 e10 b64 & CNN1b64 & 1 & 64 & 5 & 0.5 & 10 & 64 \\ cmn\_cl1\_64-f85 d5 e7 b16 & CNN1e7 & 1 & 64 & 5 & 0.5 & 7 & 16 \\ cmn\_cl1\_64-f85 d5 e12 b16 & CNN1e12 & 1 & 64 & 5 & 0.5 & 12 & 16 \\ cmn\_cl1\_64-f85 d3 e7 b16 & CNN1d3e7 & 1 & 64 & 5 & 0.3 & 7 & 16 \\ cmn\_cl1\_64-f85 d4 e10 b16 & CNN1d4 & 1 & 64 & 5 & 0.4 & 10 & 16 \\ cmn\_cl1\_64-f83 d4 e10 b16 & CNN1e3 & 1 & 64 & 3 & 0.4 & 10 & 16 \\ cmn\_cl1\_f12-f83 d4 e10 b16 & CNN1f128 & 1 & 128 & 3 & 0.4 & 10 & 16 \\ cmn\_cl1\_f26-f83 d4 e10 b16 & CNN1f256 & 1 & 256 & 3 & 0.4 & 10 & 16 \\ cmn\_cl2\_64-f83 d4 d4 e10 b16 & CNN2f54 & 2 & 64 & \(3,4\) & 0.4 & 10 & 16 \\ cmn\_cl2\_64-f845 d4 e10 b16 & CNN2f45 & 2 & 64 & \(4,5\) & 0.4 & 10 & 16 \\ cmn\_cl3\_64-f8345 d4 e10 b16 & CNN3f345 & 3 & 64 & \(3,4,5\) & 0.4 & 10 & 16 \\ \hline \multicolumn{8}{c}{CNNBOSentiment} \\ \hline cmnBOS\_cl1\_f32\_f33\_d4\_e10\_b16 & CNNBOS1 & 1 & 32 & \(3\times 3\) & 0.4 & 10 & 16 \\ cmnBOS\_cl1\_f32\_f833 d4\_e10\_b32 & CNNBOS3b32 & 1 & 32 & \(3\times 3\) & 0.4 & 10 & 32 \\ cmnBOS\_cl1\_f32\_f833 d4 e10 b64 & CNNBOSb6d4 & 1 & 32 & \(3\times 3\) & 0.4 & 10 & 64 \\ cmnBOS\_cl1\_f32\_f844 d4 e10 b16 & CNNBOSf484 & 1 & 32 & \(4\times 4\) & 0.4 & 10 & 16 \\ cmnBOS\_cl1\_f46\_stat\_d4\_e10\_b16 & CNNBOSf64 & 1 & 64 & \(4\times 4\) & 0.4 & 10 & 16 \\ cmnBOS\_cl1\_f28\_stat\_d4\_e10\_b16 & CNNBOSf6128 & 1 & 128 & \(4\times 4\) & 0.4 & 10 & 16 \\ cmnBOS\_cl1\_f256\_stat\_d4\_e10\_b16 & CNNBOSf256 & 1 & 256 & \(4\times 4\) & 0.4 & 10 & 16 \\ \hline \hline \end{tabular} \({}^{a}\): Number of convolution layers. \({}^{b}\): Number of filters. \({}^{c}\): Filters size. \({}^{d}\): Dropout. \({}^{e}\): Number of epochs. \({}^{f}\): Batch size.  
\end{table} 
Table 1: An overview of the convolutional neural network (CNN) models and CNN models with the BOSentiment model. Models were created based on different numbers of convolutional layers, filters, and their sizes, epochs, and batch size. The confusion matrix [83] was used as a visual representation of the evaluation of the performance of the models. In Table 2 there are several measures used to evaluate classifier performance -precision, recall, \(F_{1}\) score, accuracy, and the Hamming loss score. The Table 2 shows the classification report for various classifiers based on their precision, \(F_{1}\) score, mean squared error (\(\sigma_{M}\)), standard deviation (\(\sigma\)), and Hamming loss. Each classifier is listed with its corresponding precision score for each class \((-1,0,1)\) and the \(F_{1}\) score. The \(\sigma_{M}\) and \(\sigma\) values indicate the classifier's error and dispersion, respectively, while the Hamming loss measures the fraction of unclassified labels. The best-performing classifier according to the table is BOSentiment with the highest precision score for all classes and the highest \(F_{1}\) score of 0.89. It also has the lowest \(\sigma_{M}\) and \(\sigma\) values, indicating the smallest error and dispersion, respectively.