# A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
## 4 Experiment
### Experimental Results  
Let the sentiment classification (SC) models enhanced by S-EWE\({}_{\text{c}}\) and S-EWE\({}_{\text{f}}\) be denoted by S\({}_{\text{S-EWE}_{\text{c}}}\) and S\({}_{\text{S-EWE}_{\text{f}}}\), respectively. When Word2 Vec was used to pre-train word embeddings for sentiment classification tasks on SemEval-2013 [27] and SST-2 [28] datasets, the accuracies and macro-F1 scores under the sentiment classification models TextCNN, TextRNN, TextRCNN, TextBiRCNN, ABCDM, and CNN-LSTM with and without S-EWE\({}_{\text{c}}\) and S-EWE\({}_{\text{f}}\) are shown in Table 5. We conclude that: (1) the sentiment classification models with Word2 Vec enhanced by S-EWE\({}_{\text{f}}\) and S-EWE\({}_{\text{c}}\) had higher accuracies and macro-F1 than the original Word2 Vec except for TextRNN and CNN-LSTM; (2) in most cases, the sentiment classification models with Word2 Vec enhanced by S-EWE\({}_{\text{c}}\) achieved the best accuracies and macro-F1 scores on two datasets; (3) TextRNN with Word2 Vec enhanced by S-EWE\({}_{\text{f}}\) and S-EWE\({}_{\text{c}}\) achieved the same accuracies and macro-F1 scores of the TextRNN with original Word2 Vec--that is, the S-EWE method did not improve the performance of TextRNN on the two datasets; and (4) on the two datasets, the variance of the results obtained by the model using the proposed method was minor, indicating that using S-EWE method can make the model more stable. When GloVe is used to pre-train word embeddings for sentiment classification tasks on SemEval-2013 [27] and SST-2 [28] datasets, the accuracy and maro-F1 scores under sentiment classification models, including TextCNN, TextRNN, TextRCNN, TextBiRCNN, ABCDM, and CNN-LSTM, enhanced by S-EWE\({}_{\text{c}}\) and S-EWE\({}_{\text{f}}\) are shown in Table 6. Compared with these results, we can conclude that: (1) the sentiment classification models with GloVe enhanced by S-EWE\({}_{\text{f}}\) and S-EWE\({}_{\text{c}}\) had higher accuracies and macro-F1 scores than the original GloVe except for TextRNN; (2) in most cases, the sentiment classification models with GloVe enhanced by S-EWE\({}_{\text{c}}\) achieved the best accuracies and macro-F1 scores on two datasets; (3) TextRNN is not trainable on these two datasets with the proposed method; and (4) models using our method achieved more robust results on both datasets. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **S-EWE** & **TextCNN** & **TextRNN** & **TextRCNN** & **TextBiRCNN** & **ABCDM** & **CNN-LSTM** \\ \hline epochs & 200 & 40 & 40 & 40 & 40 & 100 & 100 \\ batch size & 64 & 64 & 64 & 128 & 128 & 64 & 64 \\ dropout & - & 0.5 & 0.5 & 1.0 & 1.0 & 0.5 & 0.5 \\ learning rate & \(1\times 10^{-3}\) & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) \\ early stop & 512 & 256 & 256 & 512 & 512 & 512 & 512 \\ filter sizes & - & \((3,4,5)\) & - & - & - & \((4,6)\) & 3 \\ number of filters & - & 100 & - & - & - & 32 & 600 \\ hidden sizes & - & - & 128 & 256 & 256 & 128 & 600 \\ hidden layers & - & - & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter details of different models.