## 2 Related Work
### Valence and Arousal  
[PERSON] et al. [14] proposed the approach of using Valence and Arousal for Chinese sentiment analysis. Valence and Arousal can be represented by a two-dimensional vector space. Valence is a measure of the degree of sentiment, and it ranges from 1 to 9, which represents negative to positive emotion, respectively. Arousal is the level of emotional agitation represented by values also from 1 to 9, with higher ones representing more agitated emotion and lower ones representing calm. [PERSON] et al. [15] proposed a weighted graph model that considers both the relations of multiple nodes and their similarities as weights to automatically determine the VA ratings of affective words. Experiments on Chineseaffective lexicons show that this method yielded a smaller error rate on VA prediction than the linear regression, kernel method, and PageRank algorithms used in previous studies.  
Subsequently, the Dimensional Sentiment Analysis for Chinese Phrases (DSAP) shared task was held at the 8 th International Joint Conference on Natural Language Processing (IJCNLP 2017). It consisted of two subtasks, including the evaluations of words and phrases. There were 2802 words in the training dataset and 750 in the testing dataset, and they have been merged into the Chinese Valence-Arousal Words (CVAW) 3.0 ([[http://nlp.innobic.yzu.edu.tw/resources/cvaW.html](http://nlp.innobic.yzu.edu.tw/resources/cvaW.html)]([http://nlp.innobic.yzu.edu.tw/resources/cvaW.html](http://nlp.innobic.yzu.edu.tw/resources/cvaW.html))) corpus. Figure 2 shows a scatter plot of 3552 words in CVAW 3.0 and their corresponding Valence and Arousal values. For example, with the word \"joy\", which has V: 7.4 and A: 6.2, a Valence value of 7.4 shows that the word's tendency is towards the positive emotions, and an Arousal value of 6.2 further indicates that the emotion is not overly agitated but is also not completely calm. In another example, the word \"lost\", with V: 3 and A: 3.3, signals a negative and subtle mood. To predict Valence and Arousal ratings, [PERSON] et al. [16] suggested an approach using a densely linked LSTM network and word features. Notably, they used word embedding along with part-of-speech (POS) and word clusters as additional features to train the LSTM. The findings indicated that the system's ability in predicting valence and arousal dimensions for Chinese words is outstanding. [PERSON] et al. [17] suggested a framework that primarily employs multi-layer neural networks, with a stack of input features such as word embedding, POS-tags (POST), word clustering, prefix, character embedding, and cross sentiment information. The model was optimized with AdaBoost. This method was ranked 2 nd in the final round. [PERSON] et. al. [18] combined three models, namely, E-HowNet-based, embedding-based, and character-based models, to construct a model for this competition.  
Some recent research has improved the effectiveness of sentiment analysis; for example, [PERSON] et al. [19] described an approach that can strengthen the model by offering more refined emotional knowledge to improve the effectiveness of film recommendation systems. [PERSON] et al. [20] devised a tree-structured regional CNN-LSTM network made up of two components: regional CNN and LSTM. Experimental findings on various datasets indicated that this approach exceeded the lexicon- and regression-based models, as well  
Figure 2: Scatter plot of VA values of words in the Chinese Valence-Arousal Words (CVAW) 3.0 dataset.  
as the different neural networks previously proposed. To sum up, Valence and Arousal measures can more clearly detect the emotional content of each word, as well as effectively improve sentiment analysis models. Thus, this study builds on previous work and employs a VA-infused Bi-directional LSTM network for sentiment analysis of government social media content.