# A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
## 4 Experiment
### Comparisons with the Contextualized Word Embedding Models and the BERT with/without the S-EWEs
\begin{table}
\begin{tabular}{c|c|c c} \hline \hline
**Input Embedding** & **Model** & **Dataset** & **SemEval-2013** & **SST-2** \\ \hline \multirow{10}{*}{Word2 Vec} & TextCNN [22] & 19.36 & 18.54 \\  & TextCNN\({}_{\text{S-EWE}_{t}}\) & 21.08 (8.88\% \(\uparrow\)) & 28.76 (55.12\% \(\uparrow\)) \\  & TextCNN\({}_{\text{S-EWE}_{t}}\) & 21.50 (11.05\% \(\uparrow\)) & 25.36 (36.79\% \(\uparrow\)) \\ \cline{2-4}  & TextRNN [23] & 67.50 & 54.49 \\  & TextRNN\({}_{\text{S-EWE}_{t}}\) & 68.86 (2.01\% \(\uparrow\)) & 54.49 (0\%) \\  & TextRNN\({}_{\text{S-EWE}_{t}}\) & 64.71 (\(-\)4.13\% \(\downarrow\)) & 54.30 (\(-\)0.35\% \(\downarrow\)) \\ \cline{2-4}  & TextRCNN [24] & 392.75 & 459.07 \\  & TextRCNN\({}_{\text{S-EWE}_{t}}\) & 526.43 (34.04\% \(\uparrow\)) & 346.01 (-24.63\% \(\downarrow\)) \\  & TextRCNN\({}_{\text{S-EWE}_{t}}\) & 536.14 (36.51\% \(\uparrow\)) & 499.65 (8.84\% \(\uparrow\)) \\ \cline{2-4}  & TextBiRCNN [24] & 770.44 & 647.97 \\  & TextBiRCNN\({}_{\text{S-EWE}_{t}}\) & 825.24 (71.1\% \(\uparrow\)) & 647.19 (\(-\)0.12\% \(\downarrow\)) \\  & TextBiRCNN\({}_{\text{S-EWE}_{t}}\) & 925.44 (20.12\% \(\uparrow\)) & 715.88 (10.48\% \(\uparrow\)) \\ \cline{2-4}  & ABCDM [25] & 97.04 & 46.77 \\  & ABCDMs\({}_{\text{S-EWE}_{t}}\) & 47.35 (\(-\)**51.21\% \(\downarrow\))** & 60.61 (29.59\% \(\uparrow\)) \\  & ABCDMs\({}_{\text{S-EWE}_{t}}\) & 53.70 (\(-\)44.66\% \(\downarrow\)) & 61.65\% (31.82\% \(\uparrow\)) \\ \cline{2-4}  & CNN-LSTM [26] & 190.94 & 71.09 \\  & CNN-LSTM\({}_{\text{S-EWE}_{t}}\) & 174.97 (\(-\)8.36\% \(\downarrow\)) & 177.24 (149.32\% \(\uparrow\)) \\  & CNN-LSTM\({}_{\text{S-EWE}_{t}}\) & 105.59 (\(-\)44.70\% \(\downarrow\)) & 384.11 (**440.32\% \(\uparrow\)**) \\ \hline \multirow{10}{*}{GloVe} & TextCNN [22] & 18.12 & 34.44 \\  & TextCNN\({}_{\text{S-EWE}_{t}}\) & 21.93 (21.03\% \(\uparrow\)) & 29.07 (\(-\)15.59\% \(\downarrow\)) \\  & TextCNN\({}_{\text{S-EWE}_{t}}\) & 21.74 (19.98\% \(\uparrow\)) & 31.55 (\(-\)8.39\% \(\downarrow\)) \\ \cline{2-4}  & TextRNN [23] & 66.32 & 55.65 \\  & TextRNN\({}_{\text{S-EWE}_{t}}\) & 66.78 (0.69\% \(\uparrow\)) & 70.94 (27.48\% \(\uparrow\)) \\  & TextRNN\({}_{\text{S-EWE}_{t}}\) & 66.63 (0.47\% \(\uparrow\)) & 69.94 (25.68\% \(\uparrow\)) \\ \cline{2-4}  & TextRCNN [24] & 339.23 & 576.37 \\  & TextRCNN\({}_{\text{S-EWE}_{t}}\) & 390.31 (15.06\% \(\uparrow\)) & 461.90 (\(-\)19.86\% \(\downarrow\)) \\  & TextRCNN\({}_{\text{S-EWE}_{t}}\) & 437.42 (28.94\% \(\uparrow\)) & 424.92 (\(-\)26.28\% \(\downarrow\)) \\ \cline{2-4}  & TextBiRCNN [24] & 822.67 & 719.04 \\  & TextBiRCNN\({}_{\text{S-EWE}_{t}}\) & 822.35 (\(-\)0.04\% \(\downarrow\)) & 790.64 (9.96\% \(\uparrow\)) \\  & TextBiRCNN\({}_{\text{S-EWE}_{t}}\) & 824.52 (0.22\% \(\uparrow\)) & 794.55 (10.50\% \(\uparrow\)) \\ \cline{2-4}  & ABCDM [25] & 47.59 & 74.07 \\  & ABCDMs\({}_{\text{S-EWE}_{t}}\) & 64.50 (35.53\% \(\uparrow\)) & 78.82 (6.41\% \(\uparrow\)) \\  & ABCDMs\({}_{\text{S-EWE}_{t}}\) & 119.70 (151.52\% \(\uparrow\)) & 95.43\% (28.84\% \(\uparrow\)) \\ \cline{2-4}  & CNN-LSTM [26] & 164.84 & 189.07 \\  & CNN-LSTM\({}_{\text{S-EWE}_{t}}\) & 159.77 (\(-\)3.08\% \(\downarrow\)) & 222.25 (17.55\% \(\uparrow\)) \\  & CNN-LSTM\({}_{\text{S-EWE}_{t}}\) & 121.59 (\(-\)26.24\% \(\downarrow\)) & 110.91 (\(-\)41.34\% \(\downarrow\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparisons with the convergence times of TextCNN, TextRNN, TextRCNN, TextBiRCNN, ABCDM, and CNN-LSTM using Word2 Vec and GloVe with or without S-EWE\({}_{c}\) and S-EWE\({}_{f}\) on two datasets. The contents in parentheses indicate the increased or decreased convergence speeds compared to the original models. Finally, the representation will be fed into one fully connected layer with a softmax function to predict sentence sentiment. **BERT with S-EWEs**. We extract the weights in the embedding layer of BERT and use S-EWE\({}_{f}\) and S-EWE\({}_{c}\) to enhance these weights. After enhancing the sentiment of the BERT embeddings, we replace these weights with the S-EWEs. Finally, we feed them into the BERT model.