Early Recognition of User-Generated Content Value with Text Semantics and Associative Network Dual-Link Fusion
Wang Song1(),Luo Ying1,Liu Xinmin2
1College of Economics & Management, Shandong University of Science and Technology, Qingdao 266590, China 2College of Economics & Management, Qingdao Agricultural University,Qingdao 266109, China
[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.
王松, 骆莹, 刘新民. 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*[J]. 数据分析与知识发现, 2023, 7(11): 101-113.
Wang Song, Luo Ying, Liu Xinmin. Early Recognition of User-Generated Content Value with Text Semantics and Associative Network Dual-Link Fusion. Data Analysis and Knowledge Discovery, 2023, 7(11): 101-113.
(Wang Nan, Chen Xiangxiang, Qi Yunli, et al. The Research on Influence Factors of User Innovation Community Idea Adoption Based on Elaboration Likelihood Model[J]. Chinese Journal of Management Science, 2020, 28(3): 213-222.)
(Shi Jiarong, Ma Yuanyuan. Research Progress and Development of Deep Learning[J]. Computer Engineering and Applications, 2018, 54(10): 1-10.)
doi: 10.3778/j.issn.1002-8331.1712-0418
[5]
Sherstinsky A. Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network[J]. Physica D: Nonlinear Phenomena, 2020, 404: 132306.
doi: 10.1016/j.physd.2019.132306
[6]
Chaudhari S, Mithal V, Polatkan G, et al. An Attentive Survey of Attention Models[J]. ACM Transactions on Intelligent Systems and Technology, 2021, 12(5): 1-32.
[7]
李德顺. 价值论: 一种主体性的研究[M]. 第3版. 北京: 中国人民大学出版社, 2013.
[7]
(Li Deshun. Axiology: A Study of Subjectivity[M]. The 3rd Edition. Beijing: China Renmin University Press, 2013.)
(Tang Xiaobo, Xiang Lili, Mou Hao. Early Identification Method of Academic Value of Papers Based on Research Question and Research Method Contribution[J]. Information Science, 2022, 40(9): 3-11, 19.)
(Wang Song, Yang Yang, Liu Xinmin. Discovering Potentialities of User Ideas from Open Innovation Communities with Graph Attention Network[J]. Data Analysis and Knowledge Discovery, 2021, 5(11): 89-101.)
(Li Lei, Zhang Linlin, Wang Ao, et al. Quality Evaluation of Academic User Generated Content on Social Media[J]. Information Studies: Theory & Application, 2023, 46(2): 175-183.)
(Zhou Zhi, Li Mingzi, Cui Xu. Research on Helpfulness Evaluation of User Generate Content Based on Domain Sentiment Lexicon: Taking Douban Reading as an Example[J]. Information Studies: Theory & Application, 2022, 45(1): 86-92.)
doi: 10.16353/j.cnki.1000-7490.2022.01.012
(Hong Chuang, Li He, Mao Taitian. Study on the Adoption Mechanism of Knowledge Contribution from Open Innovation Community Users[J]. Journal of Modern Information, 2020, 40(5): 33-40.)
doi: 10.3969/j.issn.1008-0821.2020.05.005
(Tao Xiaobo, Xu Pengyu, Fan Chao, et al. Research on the Influence Mechanism of Information Adoption Behavior of New Product Developers in Innovation Community[J]. Management Review, 2020, 32(10): 135-146.)
[14]
Han C J, Yang M. Stimulating Innovation on Social Product Development: An Analysis of Social Behaviors in Online Innovation Communities[J]. IEEE Transactions on Engineering Management, 2022, 69(2): 365-375..
doi: 10.1109/TEM.2019.2955073
[15]
Zhang M, Fan B, Zhang N, et al. Mining Product Innovation Ideas from Online Reviews[J]. Information Processing & Management, 2021, 58(1): 102389.
doi: 10.1016/j.ipm.2020.102389
(Yi Ming, Li Huoran, Liu Jiyue. Research on Online Discussion Information Classification Model Based on GloVe-BiLSTM[J]. Information Studies: Theory & Application, 2022, 45(9): 173-179.)
doi: 10.16353/j.cnki.1000-7490.2022.09.023
(Han Pu, Zhang Wei, Zhang Zhanpeng, et al. Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel[J]. Data Analysis and Knowledge Discovery, 2021, 5(11): 68-79.)
(Wang Lanlan, Yao Chunlong, Li Xu, et al. Combining Dependency Syntactic Parsing with Interactive Attention Mechanism for Implicit Aspect Extraction[J]. Application Research of Computers, 2022, 39(1): 37-42.)
(Zhang Jidong, Jiang Liping. Research on Irony Recognition of Travel Reviews Based on Multi-Modal Deep Learning[J]. Information Studies: Theory & Application, 2022, 45(7): 158-164.)
doi: 10.16353/j.cnki.1000-7490.2022.07.022
(Jiang Yuxiao, Ding Shengchun, Wu Peng. A Study on the Classification of Features of Multi-Modal Information Based on BiLSTM-VGG16[J]. Information Studies: Theory & Application, 2021, 44(11): 180-186, 179.)
doi: 10.16353/j.cnki.1000-7490.2021.11.024
(Xu Jinghang, Zuo Wanli, Liang Shining, et al. Causal Relation Extraction Based on Graph Attention Networks[J]. Journal of Computer Research and Development, 2020, 57(1): 159-174.)
[24]
Sussman S W, Siegal W S. Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption[J]. Information Systems Research, 2003, 14(1): 47-65.
doi: 10.1287/isre.14.1.47.14767
(Shen Wang, Li Shiyu, Liu Jiayu, et al. Optimizing Quality Evaluation for Answers of Q&A Community[J]. Data Analysis and Knowledge Discovery, 2021, 5(2): 83-93.)
(Yan Weiwei, Huang Wei, Wen Xin. Intelligent Quality Evaluation and Service Optimization of Q&A in Academic Social Networking Site[J]. Library and Information Service, 2021, 65(6): 129-137.)
doi: 10.13266/j.issn.0252-3116.2021.06.014
(Guo Shunli, Zhang Xiangxian, Tao Xing, et al. Research on Automated Evaluation of User Generated Answer Quality in Social Question and Answer Community—Taking “Zhihu” as an Example[J]. Library and Information Service, 2019, 63(11): 118-130.)
doi: 10.13266/j.issn.0252-3116.2019.11.013
[28]
Bonacich P. Factoring and Weighting Approaches to Status Scores and Clique Identification[J]. The Journal of Mathematical Sociology, 1972, 2(1): 113-120.
doi: 10.1080/0022250X.1972.9989806
[29]
Freeman L C. A Set of Measures of Centrality Based on Betweenness[J]. Sociometry, 1977, 40(1): 35.
doi: 10.2307/3033543
[30]
Bavelas A. Communication Patterns in Task-Oriented Groups[J]. The Journal of the Acoustical Society of America, 1950, 22(6): 725-730.
doi: 10.1121/1.1906679
(Yang Donghong, Wu Bangan, Sun Xiaochun. Research on the Helpfulness Prediction Model of Online Review Information Based on Machine Learning[J]. Information Science, 2019, 37(12): 34-39, 77.)
(Zhang Rui, He Luxin, Huang Wei. Research on Usefulness Detection of Danmaku Information in Video Websites Based on Multi-Feature Fusion[J]. Journal of Modern Information, 2022, 42(4): 99-109.)
doi: 10.3969/j.issn.1008-0821.2022.04.009
(Chen Yuangao, Ying Mengqian, Bi Ran, et al. The Moderating Effect of Manager Response on the Relationship Between Online Review and Review Helpfulness: An Empirical Study of TripAdvisor[J]. Journal of Industrial Engineering and Engineering Management, 2021, 35(5): 110-116.)