# Sentiment Classification Using Convolutional Neural Networks
## 5 Result and Discussion
### Result
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**CR**} & \multicolumn{2}{c}{**SST**} \\ \cline{2-5}  & **Weighted-F1** & **F1** & **Weighted-F1** & **F1** \\ \hline Decision Tree & 63.7 & 47.0/74.5 & 51.5 & 62.4/41.7 \\ Naive Bayes & 61.0 & 77.7/31.8 & 35.7 & 10.6/63.4 \\ Support Vector Machine & 59.7 & 26.5/78.5 & 37.8 & 68.6/4.0 \\ Random Forest & 64.4 & 41.8/76.9 & 51.2 & 59.0/47.3 \\ \hline [PERSON] [1] & 74.8 & 78.6/65.6 & 56.1 & 47.2/66.3 \\ [PERSON] et al. [42] & 54.8 & 64.7/37.7 & 52.1 & 45.6/59.5 \\ \hline Emb+Conv+Conv+Pool+FC & 78.3 & 84.8/67.1 & 68.3 & 70.5/65.7 \\ Emb+Conv+Pool+FC & 78.3 & 82.3/71.3 & 66.5 & 67.7/65.1 \\ Emb+Conv+Conv+Conv+Pool+FC & 70.4 & 82.0/50.3 & 68.2 & 68.4/68.0 \\ Emb+Conv+Pool+Conv+FC & 75.3 & 84.0/60.3 & 68.6 & 70.5/66.5 \\ Emb+Conv+globalpool+FC & 81.4 & 86.1/73.4 & 70.2 & 72.8/67.2 \\ Emb+Conv+Conv+CO+explopool+FC & 79.4 & 84.5/70.5 & 70.0 & 71.2/68.7 \\ Emb+Conv+Pool+Conv+Pool+FC & 73.18 & 82.8/56.5 & 66.62 & 67.7/65.4 \\ Emb+Conv+Pool+Conv+Pool+Conv+Pool+FC & 51.57 & 77.6/6.7 & 65.21 & 70.2/59.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results with the CR and SST data, where Emb, Conv, Pool, globalpool, FC stand for embedding layer, convolutional layer, pooling layer, global pooling layer, and fully-connected layer, respectively. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** & **Weighted-F1** \\ \hline Decision Tree & 59.64 & 58.0/64.0 & 76.2/39.6 & 67.4/47.1 & 57.2 \\ Naive Bayes & 56.40 & 57.5/53.3 & 77.7/30.7 & 66.1/38.9 & 52.0 \\ Support Vector Machine & 54.95 & 57.7/55.2 & 93.0/9.1 & 69.3/15.5 & 44.9 \\ Random Forest & 58.73 & 56.4/59.8 & 74.7/39.4 & 66.4/46.4 & 58.1 \\ \hline [PERSON] [1] & 80.85 & 80.7/80.9 & 76.2/84.7 & 78.3/82.8 & 80.75 \\ [PERSON] et al. [42] & 77.28 & 72.4/69.1 & 56.1/82.1 & 63.2/75.0 & 69.62 \\ \hline Emb+Conv+Conv+Pool+FC & 81.06 & 81.5/80.7 & 75.6/85.6 & 78.4/83.1 & 80.96 \\ Emb+Conv+Pool+FC & 79.70 & 77.4/81.63 & 78.2/80.9 & 77.8/81.3 & 79.71 \\ Emb+Conv+Conv+Conv+Pool+FC & 80.30 & 80.2/80.4 & 75.3/84.5 & 77.7/82.4 & 80.26 \\ Emb+Conv+Pool+Conv+FC & 78.17 & 74.4/81.8 & 79.5/77.1 & 76.8/79.4 & 78.22 \\ Emb+Conv+globalpool+FC & 77.54 & 77.3/77.7 & 71.8/82.4 & 74.4/79.9 & 77.39 \\ Emb+Conv+Conv+globalpool+FC & 79.06 & 79.1/79.0 & 73.5/83.8 & 76.2/81.3 & 78.98 \\ Emb+Conv+Pool+Conv+Pool+FC & 79.11 & 78.6/79.5 & 74.3/83.1 & 76.4/81.2 & 79.0 \\ Emb+Conv+Pool+Conv+Pool+Conv+Pool+FC & 74.61 & 84.1/72.8 & 59.5/90.6 & 69.7/80.7 & 75.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results with the MR data, where Emb, Conv, Pool, globalpool, FC stand for embedding layer, convolutional layer, global pooling layer, and fully-connected layer, respectively. shallow networks will underfit. For example, although the third network was deeper than the first network, the first network outperformed the third network, as shown in Table 3. To clarify this, we conducted experiments varying the number of convolutional layers from 1-5, and its result is depicted in Figure 2. Meanwhile, the first network can be compared with the fourth network to show that stacking two consecutive convolutional layers is better. The fifth and sixth networks may be compared with the first and second networks, if one can see if the max-pooling and the global max-pooling contribute to the models. The seventh and eighth networks had similar structures to that in [44; 45]. We also conducted ternary classification. The binary classification assumed that there were only two labels (e.g., positive and negative), whereas the ternary classification assumed that there were three labels (e.g., positive, negative, and neutral). As shown in Table 5, our first network was the best for the ternary classification, but the performance fell to a 68.31% weighted-F1 score.