# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 5 Conclusions and Future Work  
Research on social network emotions holds significant importance for personal development, business marketing, administration, politics, and more. Emotion analysis in social networks plays a crucial role in effectively managing the development and evolution of public opinion. However, existing research mostly categorizes users' emotions into two or three broad categories, such as positive, negative, and neutral. There is a scarcity of detailed classification research on emotions in Chinese short texts. Analyzing user emotions from generated text information has become an urgent problem. In light of this, we combine the current methodology of dual learning and focus on analyzing the six-element emotions in Chinese text information. Our work involves the following tasks:  
* In order to enhance the recognition and classification of emotions and address the issue of poor classification performance due to limited training data for emotional subcategories, we employ the concept of dual learning. This approach involves mutual training and learning between the original model and the dual model using a smaller amount of labeled data. In our implementation, we utilize the recurrent neural network LSTM within the framework of deep learning technology to construct both the original model and the dual model. We also provide a detailed explanation of the parameter composition and its mathematical expression. * Our findings indicate that agent models with the same orientation exhibit similar functionalities with certain distinctions. Consequently, we generate multiple primal-dual model pairs by combining the aforementioned approaches with varying initialization and data access sequences. In order to enhance sentiment classification results, we propose a dual dual inference sentiment classification framework. This is accomplished by utilizing different initialization and data access sequences. By employing seeds, we generate multiple distinct dual models and original models. These similar models can then be utilized to reason a single task in the same direction, thereby achieving improved sentiment classification outcomes. * In order to validate the effectiveness of our proposed model in the field of emotion classification, we conducted a comprehensive set of comparative experiments specifically targeting the six-element emotion classification. Firstly, we conducted a macro-level analysis to compare the classification performance of our proposed model against other models such as LSTM, Bi-LSTM, CNN+BiLSTM, C-GRU, and MF-CSEL. Subsequently, we performed a microscopic analysis focusing on the six-element sentiment classification of our proposed model. Additionally, we separately analyzed the hyperparameters involved in the experiments. The results of the comparisons effectively demonstrate the efficacy of our proposed model. The experiments also reveal that our proposed model exhibits notable improvements in sentence generation tasks, thereby highlighting its feasibility and potential. However, our proposed model does bear certain limitations. To address this, we aim to enhance and optimize our model by collecting a wider range of sentiment data using web crawlers and other methods. This will enable us to extend the applicability of our model to other language datasets. Additionally, we believe that integrating our proposed models with key node characteristics in social networks for sentiment analysis research can provide valuable insights into opinion trends in specific domains. Conceptualization & writing, X.H. ; Review & editing, Y.D. ; Writing--original draft & experimental verification, Y.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the National Natural Science Foundation of China (Grant No. 61872298), and the Science and Technology Department of Sichuan Province (No. 2021 YFCQ0008, No. 2023 YFQ0044). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.