# A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
## 5 Conclusions  
This paper proposes a sentiment enhancement method, i.e., the sentiment-enhanced word embedding method. This model finds the relationship between the words in the sentiment lexicon and their corresponding sentiment orientations. After training the sentiment mapping matrix, this matrix and word embeddings are fused as sentiment-enhanced word embeddings. Then, the sentiment-enhanced word embeddings are fed into sentiment classification models to classify the sentiment orientations of sentences on SemEval-2013 and SST-2 datasets. Experimental results show that these models using Word2 Vec and GloVe enhanced by the sentiment-enhanced word embeddings perform better than those with original Word2 Vec and GloVe embeddings. Moreover, the convergence times of the models using Word2 Vec and GloVe enhanced by the sentiment-enhanced word embeddings were acceptable. Since the S-EWE method only enhances the embedding layer of the model and has no effect on the model itself, it does not effectively enhance contextualized word embedding.  
In the future, there will be some sustainable research directions. (1) The proposed sentiment mapping embeddings could be considered external knowledge in other sentiment classification models. (2) Since sentiment orientations contain less information than sentiment intensity, sentiment intensity could be considered in the S-EWE method. (3) Since Chinese words contain one or more tokens, various Chinese sentiment-enhanced word embedding methods need further study by expanding the S-EWE method. (4) The  
\begin{table}
\begin{tabular}{c c c|c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c|}{**SemEval-2013**} & \multicolumn{2}{c}{**SST-2**} \\ \cline{2-5}  & **Accuracy** & **Macro-F1** & **Accuracy** & **Macro-F1** \\ \hline CoSE-T \({}^{\ddagger}\) & 90.80 & - & 89.80 & - \\ BERT & 90.80 & 88.88 & 90.38 & 90.38 \\ \hline BERT\({}_{\text{S-EWE}_{t}}\) & 81.66 & 77.54 & 79.41 & 79.40 \\ BERT\({}_{\text{S-EWE}_{c}}\) & 75.83 & 68.67 & 72.66 & 72.65 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Theaccuracies and macro-F1 scores of BERT (with and without S-EWE) and CoSE-T on SemEval-2013 and SST-2 datasets (%). \({}^{\ddagger}\) represents that the results are from [6].  
methods of injecting sentiment into contextualized word embeddings, such as BERT, need to be used in sentiment analysis.  
Methodology, software, data curation and writing--original draft, Q.L.; supervision, writing--review and editing, funding acquisition, and formal analysis, X.L.; funding acquisition, investigation, and validation, Y.D.; formal analysis and software, Y.F.; funding acquisition and formal analysis, X.C. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the Sichuan Science and Technology Program (2022 YFG0378, 2021 YPG0008), the National Natural Science Foundation of China (61802316, 61872298, 61902324) and the Innovation Fund of Postgraduate, Xihua University (grant number YCJJ2021025).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request. All source codes and experiment details are available at [[https://github.com/Balding-Lee/ESWV](https://github.com/Balding-Lee/ESWV)]([https://github.com/Balding-Lee/ESWV](https://github.com/Balding-Lee/ESWV)), accessed on 13 March 2022.  
The authors declare no conflict of interest.  
The authors would like to thank all editors and anonymous reviewers for their valuable comments and suggestions, which have significantly improved the quality and presentation of this paper.