# Sentiment Classification Using Convolutional Neural Networks
## 5 Result and Discussion  
### Result  
The three datasets (movie review data, customer review data, and Stanford Sentiment Treebank data) were applied to the proposed CNN models, traditional machine-learning models, and other state-of-the-art models. Note that, we conducted two experiments: binary classification and ternary classification. Tables 3 and 4 show the experimental results of binary classification, where the two values for each cell correspond to the \"positive\" and \"negative\" classes, respectively. Table 5 shows the experimental results of ternary classification in MR data. Table 3 shows the experimental results with \begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Description** \\ \hline \multirow{2}{*}{Naive Bayes (NB)} & - Probabilistic classifier based on the Bayes’ theorem \\  & - Assumes the independence between features \\ \hline \multirow{6}{*}{Decision Tree (DT)} & - C4.5 classifier using the J48 algorithm \\  & - Confidence factor for pruning: 0.25 \\ \cline{1-1}  & - Minimum number of instances: 1 \\ \cline{1-1}  & - The number of decimal places: 1 \\ \cline{1-1}  & - Determines the amount of data used for pruning: 5 \\ \cline{1-1}  & - Reduced error pruning: True \\ \hline \multirow{6}{*}{Support Vector Machine (SVM)} & - Non-probabilistic binary classification model that finds a decision \\ \cline{1-1}  & boundary with a maximum distance between two classes \\ \cline{1-1}  & - Kernel: RBF \\ \cline{1-1}  & - Exponent = 1.0, Complexity (c) = 10.0 \\ \hline \multirow{6}{*}{Random Forest (RF)} & - Kind of ensemble model that generates final result by incorporating the \\ \cline{1-1}  & results of multiple decision trees \\ \cline{1-1}  & - Maximum tree depth: 30 \\ \cline{1-1}  & - Number of iterations: 150 \\ \cline{1-1}  & - Each tree has no depth limitation \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The description of the traditional machine learning models and the parameter settings. the MR data, while Table 4 describes the experimental results with the CR and SST data. These tables include the accuracy, precision, recall, F1 score, and weighted-F1 score. For example, the F1 scores of the decision tree are 67.2% for positive and 31.1% for negative in Table 3.  
In Table 3, the Random Forest (RF) achieved the best F1 scores, while the Decision Tree (DT) exhibited comparable results. Our first network was the best among all models including the state-of-the-art models of [1; 42]. Similarly, in Table 4, the RF and the DT achieved the best F1 scores among the traditional models, but our fifth network outperformed all other models. Note that the best networks in Tables 3 and 4 were different. That is, the first network was the best with the MR data, which had relatively longer sentences, as shown in Table 3, whereas the fifth network was the best for the other two datasets (e.g., CR, SST) that had relatively shorter sentences. One may argue that stacking more convolutional layers might be better, as the deeper network is known to capture higher level patterns. This might be true, but it should be noted that the deeper network is not always better than the shallow networks. The depth of the network needs to be determined according to the data characteristics; too deep networks will probably overfit, whereas too