## 4 Experimental Results and Discussion
This is because our method infuses emotion-specific VA features to BiLSTM with the attention mechanism, thereby effectively enhancing its ability to correctly identify the sentiment of Chinese product reviews. According to the above experiment results, our method can indeed improve the performance by providing more detailed emotional knowledge to enhance the effectiveness of sentiment classifiers, and thus it achieves remarkable performances in different types of sentiment classification datasets. The source codes of the proposed method and comparisons can be found in GitHub ([[https://github.com/yuyachengg/sentiment](https://github.com/yuyachengg/sentiment)]([https://github.com/yuyachengg/sentiment](https://github.com/yuyachengg/sentiment))). The above experiments quantitatively evaluate the performance of the proposed method. To gain a deeper insight into the Facebook fan page of the Ministry of Health and Welfare, we carried out a case study specifically on the posts in Apr. 2020. During this period, there was no local confirmed case of COVID-19 for 13 consecutive days in Taiwan; however, one case was confirmed among the _Dunmu_ navy crew employed by the Ministry of National Defense. The Ministry of Health and Welfare released 16 posts (among a total of 5744 posts) through its Facebook fan page, which attracted lively discussion from netizens. We first predicted the sentiment behind the text of posts and comments. Next, we used word clouds to visualize the categorized positive and negative keywords for each sentiment and color-coded them for clarity. It is intended for the readers to easily associate sentiments with their corresponding terms. The word cloud was built from the top fifty words with higher LLR values in each of the positive and negative sentiment categories. Moreover, we used larger font sizes for words with higher LLR weights. Figure 6 shows the resulting word cloud. We can observe that the polarity of sentiments in comments can be \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{
\begin{tabular}{c} **Evaluation** \\ **(Precision/Recall/F\({}_{1}\))** \\ \end{tabular} } & \multicolumn{4}{c}{**Method**} \\ \cline{3-6}  & & **NNLM** & **EmBERT +** & **EmBERT+LLR +** & **EmBERT+LLR+VA +** \\  & & & **BiLSTM_Att** & **BiLSTM_Att** & **BiLSTM_Att** \\ \hline \multirow{3}{*}{NLPCC 2014} & Positive & 0.758/0.789/0.773 & 0.723/0.771/0.746 & 0.762/0.762/0.762 & 0.771/0.815/0.792 \\  & Negative & 0.780/0.748/0.764 & 0.754/0.701/0.726 & 0.764/0.763/0.764 & 0.782/0.764/0.773 \\  & Macro-Avg & 0.769/0.769/0.769 & 0.738/0.736/0.736 & 0.763/0.763/0.763 & 0.776/0.789/0.782 \\ \hline \multirow{3}{*}{ECSR} & Positive & - & 0.853/0.853/0.853 & 0.901/0.913/0.907 & 0.932/0.903/0.917 \\  & Negative & - & 0.881/0.881/0.881 & 0.914/0.914/0.914 & 0.924/0.952/0.938 \\ \cline{1-1}  & Macro-Avg & - & 0.867/0.867/0.867 & 0.907/0.914/0.911 & 0.928/0.928/0.928 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performances of sentiment analysis models on different datasets. influenced by the polarity of the terms. For clarity, green terms denote positive comments and red ones are negative. Through our approach, we are not only able to easily identify that this was a post that brought positive sentiment to the public, but we could also see what the topic was that provokes sentiment among the public. For the positive sentiment part, words including \"Epidemic, Ministry of National Defense, Ministry of Health and Welfare, Entire, Government, Team, Nation\" illustrate people's affirmation of the excellent control over the epidemic in Taiwan with the cooperation of government teams as well as the whole nation. For example, one comment mentioned: \"Thank you to all the anti-epidemic personnel for their hard work and the cooperation of all Taiwanese. Fight on, Taiwan!