# Fusion Text Representations to Enhance Contextual Meaning in Sentiment Classification
## 4 Experiments and Results
### Experimental Results and Discussions  
This section discussed the results obtained from the proposed model. We compared our proposed model with several different scenarios. Table 1 shows the parameters used for model evaluation. These parameters were derived from various experimental scenarios to achieve optimal result. For our initial experiment, we conducted a comparison of the suggested model utilizing various dimensions of GloVe embeddings. However, in the case of word2 vec, we exclusively employed a single dimension, specifically 300. The GloVe dimensions utilized for comparing our suggested model were 50, 100, 200, and 300. Based on the conducted experiments, the model utilizing GloVe dimension 300 outperformed models using other GloVe dimensions, reaching an accuracy of 89.8% and an F1 score of 90.06%. This result roughly rivals the performance achieved while utilizing GloVe dimension 100. Table 2 displays comprehensive findings derived from several GloVe dimensions. GloWord is the text representation fusion model that we propose. Furthermore, we investigated the influence of different GloVe dimensions and also explored the impact of varying units in the dense layer. We employed dense layers with 32, 64, and 128 units. In the experiment evaluating the influence of the dense layer, we employed biGRU units configured to a size of 64 units. The dense layer's impact was maximized when set to 64 units, resulting in the maximum performance achieved during evaluation. Based on initial findings, it can be concluded that the optimal performance is achieved when the biGRU and dense layer have an equal number of units. The maximum performance attained an accuracy of 89.9% and an F1 score of 90.12%. Table 3 demonstrates the performance outcomes while utilizing various units in the dense layer. Table 4 shows the impact of employing various biGRU units. For this analysis, we employed a dense layer consisting of 128 units. Our objective was to confirm our initial finding that matching the number of units in the dense layer with the biGRU would improve performance. Table 4 demonstrates that employing a dense layer with 128 units and biGRU with 64 units yields inferior performance compared to the preceding outcomes in Table 3. In Table 3, the accuracy was 89.8% and the F1 score was 90.06% while utilizing the same 64 units. Nevertheless, by employing 128 units for both layers in Table 4, the \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Dimension/Unit** \\ \hline Word2 vec & 300 \\ Glove & 300 \\ biGRU & 128 \\ Dense & 128 \\ Epoch & 10 \\ Dropout & 0.4 \\ Pool & GlobalMaxPooling \\ Activation & RelLu \\ L2 Regularization & 0.001 \\ Batch size & 128 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameter. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline GloWord\_biGRU\_50 & 89.72 & 89.75 & 89.86 & 89.81 \\ GloWord\_biGRU\_100 & 89.63 & 87.32 & 92.92 & 90.03 \\ GloWord\_biGRU\_200 & 89.31 & 86.25 & 93.73 & 89.83 \\ GloWord\_biGRU\_300 & **89.8** & **88.49** & **91.68** & **90.06** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Impact of different dimension of GloVe. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline GloWord\_biGRU\_32 & 89.79 & 89.19 & 90.73 & 89.96 \\ GloWord\_biGRU\_64 & **89.9** & **89.9** & **91.37** & **90.12** \\ GloWord\_biGRU\_128 & 89.8 & 88.49 & 91.68 & 90.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Impact of different unit of dense layer. model's performance was enhanced, yielding an accuracy of 89.96% and an F1 score of 90.21%. To achieve optimal results, we utilized 128 units for both layers and examined the effects of modifying additional parameters. In Table 5, we analyze the impact of using dropout rates. We evaluated dropout rates ranging from 0.1 to 0.5.