# Research on Dual-Emotion Feature Fusion and Performance Improvement in Rumor Detection
## 3 Methodology
### Extracting the Emotional Features of Text and Comments
#### 3.4.1 Extraction of Emotional Features of Publishers  
In order to fully express the emotions of the publisher, various emotional features are extracted from the rumor text, including the emotion category, emotion vocabulary, emotion intensity, emotion score, and other auxiliary features. Of the five characteristics, the emotion category, emotion intensity, and emotion score basically define the overall emotional information of the rumor, and the other two provide information at the lexical and symbolic levels.  
1. Emotion Category: An emotion classifier can effectively help identify emotion types in a more detailed and accurate way. Here, the Baidu AI model is used to directly output category features in rumor \(T\).
2. Emotion Lexicon: High-frequency emotion words can express the specific emotions of rumors and are realized by using an emotion dictionary annotated by experts. For rumor \(T\), \(t_{i}\) is the \(i\)-th word in \(T\), and there are \(d_{e}\) emotional categories in the emotion dictionary, \(E=\left\{E_{1},E_{2},\cdots\cdot,E_{d_{e}}\right\}\).  
Consider the frequency of t\({}_{i}\) in the emotion dictionary and the semantic influence of the above and below words, such as degree words and negative words, which have the effect of changing the direction of the emotion (e.g., \"unhappy\", \"very fond\"). Secondly, the degree value of negative words and degree words in the emotion dictionary and the probability of matching emotion words are used to calculate  
\[s(t_{i},e)=neg(t_{i},w)*\deg(t_{i},w)/L \tag{5}\]  
The value \(s(T,e)\) of all words \(t\) on a particular emotion \(e\) is calculated, and the scores for each word in the range of emotion \(e\) are added:  
\[s(t,e)=\sum_{i=1}^{L}s(t_{i},e),\forall e\in E \tag{6}\]  
\(W\) represents the window size, and \(neg\left(t_{j}\right)\) and \(deg\left(t_{j}\right)\) represent the negative word degree value and degree word dimension value, respectively:  
\[neg(t_{i,w})\prod_{j=i-w}^{i-1}neg\left(t_{j}\right) \tag{7}\]  
\[deg(t_{i,w})=\prod_{j=i-1}^{i-1}deg(t_{j}) \tag{8}\]  
The scores of all the emotional texts are connected:  
\[emo\frac{lex}{T}=s(T,e_{1})\oplus s(T,e_{2})\oplus\cdots\cdots\oplus s(T,e_{ d_{e}}) \tag{9}\]
3. Emotion Category: The process of extracting lexical intensity features is similar to that of extracting emotion vocabulary features. Intensity scores are involved here. For example, the intensity value of ecstasy is higher than that of happiness. First, the perceived intensity-level score of the text is calculated: \[S^{\prime}(T,e)=\sum_{i=1}^{L}s^{\prime}(t_{i,e})=\sum_{i=1}^{L}int(t_{i})*s( t_{i,e}),\forall e\in E\] (10)\(int(t_{i})\) is the emotion intensity score of word \(t_{i}\) in the dictionary; if \(t_{i}\) is not in the dictionary, then \(int(t_{i})=0\). The final emotion intensity feature is obtained by connecting the intensity perception scores: \[emo\frac{int}{T}=s^{\prime}(T,e_{1})\oplus s^{\prime}(T,e_{2})\oplus\cdot\cdot \cdot\cdot\cdot\oplus s^{\prime}(T,e_{d_{e}})\] (11)
4. Sentiment Score: Typically, a sentiment score is a positive or negative value that represents the degree of positive and negative polarity of the entire text, and it can be calculated by using a sentiment dictionary or a public toolkit.
5. Other Auxiliary Features: Considering that the above features do not explicitly utilize information outside the affective dictionary, we introduce a set of auxiliary features to capture the affective signals of non-verbal elements, including emoticons, punctuation, sentimental words, and personal pronoun frequencies. For example, emojis and punctuation marks are universal symbols for emotional expression around the world, such as \":)\" and \":\" Such punctuation marks can convey people's emotions. The above five aspects of feature results are used to connect and extract the emotional features of publishers: \[emo_{T}=emo_{T}^{cate}\oplus emo_{T}^{lex}\oplus emo_{T}^{int}\oplus emo_{T}^{ semi}\oplus emo_{T}^{aux}\] (12)