# A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
## 4 Experiment
### Experimental Results
\begin{table}
\begin{tabular}{c|c c|c c} \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c|}{**SemEval-2013**} & \multicolumn{2}{c}{**SST-2**} \\ \cline{2-5}  & **Accuracy** & **Macro-F1** & **Accuracy** & **Macro-F1** \\ \hline TextCNN [22] & 80.02 (0.34) & 73.42 (1.22) & 79.81 (0.83) & 79.81 (0.83) \\ TextCNN\({}_{\text{S-EWE}_{t}}\) & **80.55** (0.17) & 74.13 (0.52) & **80.89** (0.15) & **80.88** (0.15) \\ TextCNN\({}_{\text{S-EWE}_{t}}\) & 80.52 (0.23) & **74.55** (0.71) & 80.69 (0.12) & 80.68 (0.12) \\ \hline TextRNN [23] & 73.07 (0) & 42.22 (0) & 49.92 (0) & 33.30 (0) \\ TextRNN\({}_{\text{S-EWE}_{t}}\) & 73.07 (0) & 42.22 (0) & 49.92 (0) & 33.30 (0) \\ TextRNN\({}_{\text{S-EWE}_{t}}\) & 73.07 (0) & 42.22 (0) & 49.92 (0) & 33.30 (0) \\ TextRNN\({}_{\text{S-EWE}_{t}}\) & 73.07 (0) & 42.22 (0) & 49.92 (0) & 33.30 (0) \\ \hline TextRNN [24] & 73.82 (0.09) & 60.31 (44.45) & 73.64 (1.13) & 73.47 (1.34) \\ TextRNN\({}_{\text{S-EWE}_{t}}\) & 74.87 (1.73) & **65.38** (2.85) & 75.65 (1.05) & 75.61 (1.1) \\ TextRNN\({}_{\text{S-EWE}_{t}}\) & **75.42** (0.19) & 64.81 (22.84) & **75.72** (0.15) & **75.66** (0.13) \\ \hline TextBiRCNN [24] & 74.07 (1.24) & 68.57 (1.14) & 74.15 (0.74) & 74.08 (0.75) \\ TextBiRCNN\({}_{\text{S-EWE}_{t}}\) & **76.67** (1.05) & 69.95 (1.03) & 75.97 (0.33) & 75.95 (0.34) \\ TextBiRCNN\({}_{\text{S-EWE}_{t}}\) & 76.13 (0.55) & **71.22** (1.89) & **76.17** (0.24) & **76.16** (0.24) \\ \hline ABCDM [25] & 74.85 (2.98) & 69.36 (0.61) & 74.26 (0.54) & 74.22 (0.59) \\ ABCDMs\({}_{\text{S-EWE}_{t}}\) & **76.08** (0.34) & 68.84 (0.52) & 75.15 (0.13) & 75.10 (0.12) \\ ABCDMs\({}_{\text{S-EWE}_{t}}\) & 75.09 (0.32) & **69.58** (1.21) & **75.51** (0.06) & **75.47** (0.06) \\ \hline CNN-LSTM [26] & **76.39** (2.54) & 68.43 (7.85) & 75.75 (0.35) & 75.73 (0.33) \\ CNN-LSTM\({}_{\text{S-EWE}_{t}}\) & 75.48 (3.17) & 68.47 (0.25) & 76.06 (0.12) & 76.01 (0.11) \\ CNN-LSTM\({}_{\text{S-EWE}_{t}}\) & 76.19 (1.35) & **69.62** (0.52) & **76.40** (0.36) & **76.38** (0.36) \\ \hline \end{tabular}
\end{table} 
Table 5: The accuracies and macro-F1 scores of different models using Word2 Vec with and without S-EWE\({}_{t}\) and S-EWE\({}_{t}\) on SemEval-2013 and SST-2 datasets (%). For each model, we ran it five times to achieve five accuracy values and macro-F1 values, respectively. Then, we took their average accuracies and macro-F1 values as the final results. The value in parentheses is the variance of the five values for each model. The bold text represents the best result achieved by each model, and the underlined text stands for the lowest variance achieved by each model. As shown in Tables 5 and 6, TextCNN, TextRCNN, TextBiRCNN, ABCDM, and CNN-LSTM with Word2 Vec or GloVe, enhanced by S-EWE\({}_{\text{c}}\) and S-EWE\({}_{\text{f}}\), achieved better classification performances than these models without the sentiment-enhanced word embeddings on the two datasets. In particular, these models with S-EWE\({}_{\text{c}}\) obtained better classification performances than those with S-EWE\({}_{\text{f}}\). TextRNN with Word2 Vec or GloVe enhanced by S-EWE\({}_{\text{c}}\) and S-EWE\({}_{\text{f}}\) kept the same classification performance as the model without the sentiment-enhanced word embedding on the two datasets. Meanwhile, models using S-EWE\({}_{\text{c}}\) and S-EWE\({}_{\text{f}}\) can achieve more robust results on the two datasets.