## 4 Experimental Results and Discussion  
We first assessed the efficiency of various models on predicting VA values of a word on the official data from the Dimensional Sentiment Analysis for Chinese Phrases (DSA_P) Competition, which included 2802 training and 750 testing instances. In our experiments, we used a dropout rate of 0.2, a batch size of 64, and the Adam optimizer with 0.01 learning rate. The VA prediction model was implemented using Keras ([[https://keras.io/](https://keras.io/)]([https://keras.io/](https://keras.io/))). Mean Absolute Error (MAE) and Pearson Correlation Coefficient (PCC) were the two metrics employed to validate these approaches. MAE's definition is as listed in Equation (2). It aims at reflecting the overall difference between actual and estimated values. Therefore, a smaller MAE indicates a better estimate. PCC provides the correlation between these values, with a range of \(-1\) to 1. A PCC that is close to 1 indicates a higher correlation between the two numbers; if a value is within 0 to 0.09, it indicates no correlation; 0.1 to 0.3 shows a low correlation; 0.3 to 0.5 a medium correlation; and greater than 0.5 indicates a clear correlation between these numbers. In Equation (3), we denote PCC as \(r\). \(A_{i}\) refers to the correct response, \(P_{i}\) refers to the outcome of the model (valence or arousal values), and \(n\) refers to the count of test samples. \(\overline{A}\) and \(\overline{P}\) are the arithmetic mean, respectively, of the previous \(A_{i}\) and \(P_{i}\), and \(\sigma\) is the standard deviation. \[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert A_{i}-P_{i}\rvert \tag{2}\]  
\[r=\frac{1}{n-1}\sum_{i=1}^{n}\left(\frac{A_{i}-\overline{A}}{\sigma_{A}} \right)\left(\frac{P_{i}-\overline{P}}{\sigma_{P}}\right) \tag{3}\]  
As shown in Table 1, the proposed method is compared with the top three competitors in the DSA_P word Valence and Arousal prediction task [30], i.e., THU_NGN, AL_I_NLP, and CKIP. Our method is outstanding in predicting the dimensional sentiment of Chinese words, achieving a comparable performance with the highest-ranking competitor (i.e.,  
Figure 5: The sentiment prediction model based on deep neural network and comprehensive features. THU_NGN). For the valence values, MAE is reduced by 0.543 and PCC increased to 88.7%. This indicates a very high correlation between the model prediction and the correct values. Similar observations can be made regarding the arousal value prediction task. The MAE is considerably lowered by 0.855, and PCC is increased to 68.9%. In the second experiment, we utilized the outputs from the prediction of valence-arousal model and integrated them into a hybrid Deep Neural Networks (DNN) for the classification of the overall sentiment of a comment on social media. Because the goal of this research is to recognize public opinion to assist government social media management, only coarse-grained sentiment categories (positive and negative) were considered in this experiment. Moreover, in order to thoroughly prove the effectiveness of the proposed model, we conducted two experiments. First, we validated the dataset from the Natural Language Processing and Chinese Computing (NLPCC) 2014 competition ([[http://tcci.ccf.org.cn/conference/2014/pages/page04_sam.html](http://tcci.ccf.org.cn/conference/2014/pages/page04_sam.html)]([http://tcci.ccf.org.cn/conference/2014/pages/page04_sam.html](http://tcci.ccf.org.cn/conference/2014/pages/page04_sam.html))) of sentiment classification for Chinese product reviews, which contained multiple domains such as books, DVDs, and electronics. There were 10,000 examples for training and 2500 for testing. In this experiment, we compared the proposed model with the best teams in this competition, which is denoted as NNLM [31].