# Fusion Text Representations to Enhance Contextual Meaning in Sentiment Classification
## 4 Experiments and Results
### Experimental Results and Discussions
Therefore, our proposed GloWord_biGRU model achieves the best performance with lower computational costs. This is evidenced by the training time, inference time, and memory consumption utilized during the training process. These results are presented in Table 8.  
Meanwhile, in Figure 3, we display the loss performance for all models. It can be observed that when using CNN, the resulting loss is the highest. This is due to CNN's inability to effectively handle sequential data. Table 9 presents the results of experiments using single word embeddings. We found that our proposed model still delivers the best performance and produces lower loss compared to using single word embeddings. The performance history during the training process with both training and validation data can be seen in Figure 4. It is evident that throughout the training process, our proposed model exhibits the best performance compared to other models when using the training data. Additionally, when using the validation data, our proposed model shows more stable performance compared to the other models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** & **Loss** \\ \hline GloWord\_CNN & 87.98 & 86.82 & 89.78 & 88.27 & 0.343 \\ GloWord\_LSTM & 89.50 & 88.87 & 90.49 & 89.68 & 0.2877 \\ GloWord\_GRU & 89.07 & 87.02 & 92.04 & 89.46 & 0.2867 \\ GloWord\_biLSTM & 89.29 & 92.00 & 86.25 & 89.03 & 0.3161 \\ GloWord\_biGRU & **89.96** & **88.7** & **91.76** & **90.21** & **0.2512** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Result with different deep learning models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Training Time** & **Inference Time** & **Memory Consumption** & **Memory Inference** \\ \hline GloWord\_biLSTM & 244.62 s & 8.62 s & 9.99 MB & 2.14 MB \\ GloWord\_biGRU & **188.8 s** & **6.06 s** & **9.51 MB** & **1.98 MB** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Time and Space Complexity. Figure 2: Accuracy performance on training and validation data among various deep learning model. In addition to comparing our results with the various scenarios we created, we also compared our findings with several previous studies. Table 10 shows the comparison results between our proposed model and several models from previous research. As seen in Table 10, our proposed model achieves the highest accuracy and F1 score among all. We compared our model with studies that used the same dataset as we did, the IMDB dataset, ensuring a balanced comparison. The results of the comparison using 5-fold cross-validation are presented in Table 11. We used the 5-fold cross-validation model as a comparison to the results of the model we proposed and as evidence that overfitting was minimal. In addition to presenting the results of each fold, we also present the average results of the five folds conducted. The results show that the best performance was achieved during the third fold, with an F1 score of 90.11%. Meanwhile, the average F1 score obtained from the 5-fold cross-validation process was 89.21%. Using the CV technique provides a more reliable estimate of performance, as its better accounts for variations in the data. This reduces the likelihood of overfitting to a particular dataset, leading to more accurate evaluation scores. Figure 4: Accuracy performance compare with single word embedding. Figure 3: Loss during training on training and validation data compare with other deep learning models.