## 2 Related Work  
### Sentiment Analysis  
Given the ubiquity of the Internet as well as the popularity of social networking platforms in recent years, the web has already become an essential field for information sharing and delivery. People also express personal opinions or discuss ongoing public issues through social networking platforms such as Facebook and Instagram. The detection of consequential information and key expression has become a popular topic in data science research. Sentiment analysis techniques are widely used to discover and extract subjective messages (e.g., point of view and manner) from the texts of social media in various fields, including especially the areas of politics, products, and movies [3]. In the political field, [PERSON] and [PERSON] [4] introduced a sentiment classification model with multiple cascades for informal short texts. The dataset under their investigation is MultiSenti, in which tweets during the 2018 general election of Pakistan were collected in order to discover the general sentiment of the public, particularly about the election process and results. Experimental results of their proposed method were compared to three previous models of multilingual sentiment classifier, and they demonstrated that their approach exceeds others overall. In addition, in the field of film, [PERSON] and [PERSON] [5] proposed a CNN-LSTM (Convolutional Neural Networks and Long Short-Term Memory) model. Their model was capable of predicting the sentiment polarity of reviews from the Internet Movie Database (IMDb) dataset with accuracy above 89%. [PERSON] et al. [6] integrated SentiWordNet features with a support vector machine to predict the polarity of movie reviews and blog posts. The result demonstrated that the vector space-based model can benefit from fusing lexical features with sentiment information. In addition, [PERSON] et al. [7] proposed the Weakness Finder, an expert framework that can help manufacturers identify the weakness of products from Chinese feedback by using aspect-based sentiment analysis. Using the morpheme-based method and the HowNet-based similarity measure, it extracted and grouped the features. Then, they defined and categorized the implicit features for each aspect with the collocation selection method. To evaluate the polarity of each aspect in phrases, they used the sentence-based sentiment analysis approach. The method has been used to help a manufacturer of body wash to recognize potential weaknesses of their product, and its performance was outstanding.  
Based on the structural units of grammar, [PERSON] [8] initiated a method for sentiment classification on the document level in 2002. Subsequently, classification of smaller units, such as sentences [9; 10], phrases [11; 12], and words [13], were developed by other researchers. It can be inferred that sentiment analysis exhibits flexibility at various levels of units in grammar. Our method manipulates the smallest one, i.e., \"words\", to conduct sentiment prediction experiments, which we then extend to sentiment classification tasks for each comment on social media so as to explore the sentiment of the public towards the government. Our method is unique in that we solve the problem of directly using the emoji ratio as the overall sentiment of a post. Therefore, we can delicately detect the sentiment and perception of the public on a post. We observe that the need to understand the public image and communication of the government to the people is very prominent, so we applied this approach in the real world to analyze the novel task of analyzing posts by the Ministry of Health and Welfare.