# Sentiment-Aware Word Embedding for Emotion Classification
## 4 Experiments
### Experimental Results  
In this section, we explain the use of the support vector machine (SVM), logistic regression model, decision tree model and gradient boost model classifier to evaluate the effectiveness of the emotional word vector. The main content is to evaluate the possible advantages of the hybrid word vectors method relative to other word representation methods. The proposed method tries different mixing methods on the emotional word vector and the semantic word vector to compare them according to their classification accuracy on all datasets. In the emotional classification task, the current widely accepted evaluation indicators are the precision and recall rate. The F-score, a comprehensive metric, was also selected to measure the accuracy of the assessment analysis. The concrete calculation process can be expressed as follows:  
\[Precision=\frac{\#system\_correc\left(emotion=i\right)}{\#system\_proposed \left(emotion=i\right)} \tag{9}\]  
\[Recall=\frac{\#system\_correct\left(emotion=i\right)}{\#gold\left(emotion=i \right)} \tag{10}\]  
where \(\#system\_correc(emotion=i)\) represents the number of texts correctly categorized into a class, and \(\#system\_proposed(emotion=i)\) represents the total number of texts classified into a class. The calculation formula for the F-score is described as follows:  
\[\textit{F-measure}=\frac{2\times\textit{Precision}\times\textit{Recall}}{ \textit{Precision}+\textit{Recall}}. \tag{11}\]  
Experiments use hybrid vectors as input into different classifiers. Here, the SVM, logistic regression classification model, decision tree model and gradient boost model are selected, and the experimental results can be described as follows. From Table 4, it can be seen that the classification accuracy and reliability of positive emotions such as happiness and trust are higher than the negative emotions such as fear and disgust. The main \begin{table}
\begin{tabular}{c c c} \hline \hline
**Emotional Categories** & **Subcategories** & **Example Words** \\ \hline HAPPINESS & happy & joy, hilarity \\  & relieved & relieved, innocent \\ TRUST & respect & polite, reverent \\  & praise & handsome, excellent \\  & believe & trust, reliability \\  & favorite & admiration, love \\  & wish & longing for, blessing \\ ANGER & anger & annoyed, furious \\ SADNESS & sad & sorrow, heart \\  & disappointed & regret, despair \\  & guilty & remorseful, unwilling, \\  & miss & lovesickness, hangs \\ FEAR & nervous & flustered, overwhelmed \\  & dread & timid, scared \\  & shame & red-faced, self-confident \\ DISGUST & bored & irritated, upset \\  & abomination & shameful, hateful \\  & blame & vanity, chaotic \\  & jealous & envy \\  & doubt & suspicious, multi-minded \\ SURPRISE & surprise & strange, miraculous \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Emotional category. reason is that in the training corpus, the text scale of positive emotion is larger than the text scale of negative emotion, for example, the number of texts belonging to happiness and trust accounted for a total of 26% of the training data, while the number of texts belonging to sadness and fear accounted for a total of 11%, which indicates that the effect of classification is related to the distribution of training corpus to a certain extent. Chinese Weibo text content is short with heavy colloquialism and many novel words on the internet, which limits the process of constructing emotional word vectors based on the emotion dictionary. Despite this, the experiment still enhanced the precision, which further proves the validity of the experimental method. The results of this experiment are related to the quality of emotional dictionaries to a certain extent, and emotional words with strong emotions should be chosen in the course of the experiment. In order to highlight the effectiveness of the emotional word vector, this study carried out comparative experiments on different word representation methods.