# Research on Dual-Emotion Feature Fusion and Performance Improvement in Rumor Detection
## 3 Methodology  
Before discussing the experimental design and implementation of rumor detection in depth, it is necessary to clarify the importance and purpose of the experiment. As a key information processing technology, the rumor detection experiment is not only a key link in verifying the effectiveness of theoretical hypotheses and algorithms but also an important driving force to promote technological progress and applications. Through well-designed experiments, we were able to systematically evaluate the performance of different rumor detection methods, including key metrics such as accuracy, recall, and F1 scores. These metrics not only help us understand how well the algorithm performs on a particular dataset but also reveal the applicability and limitations of the algorithm in different scenarios. In addition, the experimental part also undertakes the important task of exploring and optimizing the algorithm. In the process of experimentation, we can try different parameter settings, feature selections, model architectures, etc., to find the best algorithm configuration and further improve the accuracy and efficiency of rumor detection. At the same time, the diversity and complexity of the experimental data also provide us with rich scenarios to test the robustness and generalization ability of the algorithm. Therefore, in the following content, we will introduce the experimental design, dataset selection, evaluation indicators, experimental process, and preliminary analysis of the experimental results of this rumor detection experiment in detail. Through this content, we hope to present a comprehensive, in-depth, and rigorous rumor detection experimental process for readers and provide a valuable reference for subsequent research and applications. After selecting information datasets with the appropriate size and rich categories, this paper mines the emotional features in news texts and comments (i.e., emotion category, emotion intensity, emotion score, emotion vocabulary, and other auxiliary features). In order to make the following description more clear, this paper defines the features of publisher and social emotions (dual emotions). Then, the relationship and differences between dual-emotion features are analyzed, and all kinds of emotional feature sets are connected to form dual-emotion features so as to fully express dual emotions and the relationship between the two emotions. Finally, the data are inserted into the model trainer to compare the effectiveness and accuracy of each feature parameter in rumor detection using the same trainer and determine whether adding dual-emotion features can improve the accuracy of different model trainers. This paper mainly studies the following four issues:  
1. Extracting the dual-emotion features of rumors Previous studies have all been based on textual features, semantic features, or a single emotional feature of texts, but few people have paid attention to the emotions triggered by comments on rumors, and the emotions conveyed in comments usually represent people's attitudes toward the content of a rumor. Therefore, this paper extracts the dual-emotion features of comment and rumor texts, outputs a feature vector, and sends the vector in series to the MLP layer and softmax layer for the final prediction of rumor accuracy. 2. Determining whether dual-emotion features are more effective than baseline characteristics In order to prove the necessity of dual-emotion features and the importance of exploring dual-emotion features and their interrelations for rumor detection, this paper selected two baseline affective features to evaluate the validity of dual-emotion features. These features all use the same sentiment dictionary and are trained in the same model. 3. Determining whether dual-emotion features enhance the performance of the detector The characteristic components of dual emotions are publisher emotions, social emotions, and the emotion gap. An ablation study was used to determine whether they all enhance the detector's performance.