基于文本语义与关联网络双链路融合的用户生成内容价值早期识别研究  

Early Recognition of User-Generated Content Value with Text Semantics and Associative Network Dual-Link Fusion

作　　者：王松[1] 骆莹 刘新民 Wang Song;Luo Ying;Liu Xinmin(College of Economics&Management,Shandong University of Science and Technology,Qingdao 266590,China;College of Economics&Management,Qingdao Agricultural University,Qingdao 266109,China)

机构地区：[1]山东科技大学经济管理学院,青岛266590 [2]青岛农业大学经济管理学院,青岛266109

出　　处：《数据分析与知识发现》2023年第11期101-113,共13页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目(项目编号:71471105);山东省社会科学规划项目(项目编号:18CGLJ38)的研究成果之一。

摘　　要：【目的】为缓解虚拟社区中对价值性内容识别的时滞性、过载性问题,通过构建特征体系与算法模型提升早期识别的效率。【方法】综合考量用户生成内容早期的文本语义和用户、文本间显隐性交互关联的网络结构,构建双链路融合算法进行处理。在文本语义链路中,采用BERT+BiLSTM+Linear获取深层语义特征;在关联网络链路中,采纳GAT处理节点的浅层数值特征和关联特征;继而利用卷积层优化上述双链路的融合信息,最终完成价值早期识别的目的。【结果】所构建的双链路融合模型对魅族Flyme社区数据的处理准确率为89.80%,相较于单独的文本语义链路和关联网络链路,准确率分别提高了3.45和3.20个百分点。相较于其他基线模型,准确率和F1值均有不同程度的提升。【局限】模型的泛化能力有待进一步提升,缺乏对图片、外部链接等富文本内容的深入挖掘。【结论】基于深度学习融合模型对序列型文本语义、拓扑型网络结构进行综合性处理,能进一步提高对价值性文本早期识别的准确性。[Objective]This paper proposes a feature system and new model to improve the efficiency of early recognition,aiming to address the issues of time delay and overload in recognizing valuable content from virtual communities.[Methods]We constructed a dual-link fusion algorithm with the text semantics of user-generated content and the network structure of explicit and implicit interaction between users and texts.In the text semantic link,we used the BERT+BiLSTM+Linear to obtain the deep semantic features.In the association network link,we adopted GAT to process the shallow numerical characteristics and association characteristics of the nodes.Finally,we utilized the convolution layer to optimize the fusion information of the above dual links and achieved early value recognition.[Results]The dual-link fusion model had a processing accuracy of 89.80%for data from the Meizu Flyme community,which was 3.45%and 3.20%higher than that of the single text semantic link and associated network link,respectively.Compared with other baseline models,the accuracy and F1 values were also improved.[Limitations]The generalization ability of the model needs to be further improved,and we should have analyzed rich text content(i.e.,pictures and external links).[Conclusions]The deep learning fusion model improves the accuracy of early recognition of valuable texts by processing sequential text semantics and topological network structure.

关 键 词：早期识别 融合模型 双向长短期记忆网络 图注意力网络 

分 类 号：G206[文化科学—传播学]