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| Information Multi-hierarchical Classification of Online Q&A Community Oriented to Users' Needs Topics |
| Cheng Quan, Zhang Yangang |
| School of Economics and Management, Fuzhou University, Fuzhou 350116 |
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Abstract The extensiveness, convenience, interactivity and individual characteristics of the online question and answer (Q&A) community service has promoted the rapid development of the proposed model. The online Q&A community has gradually become an important platform for people to obtain various types of life-related information. However, due to the lack of effective organization of the information resources in the community and the lack of semantic relevance and other practical bottlenecks, as well as the high complexity and multi-hierarchical characteristics of such information, users’ experience in this regard is not typically satisfactory. To achieve the fine-grained organization and the semantic relationship disclosure of various information resources in the online Q&A community and subsequently achieve the goal of accurate classification of information based on user needs, a multi-hierarchical architecture system of user needs is built using the user search data in the online mother-infant community. Further, verified experimental samples of the labeled data are generated with multi-hierarchical needs themes. Finally, the classification effect of the UNT-HC (users' needs topics - hierarchical classification) is verified by comparing the information multi-hierarchical classification model (UNT-HC) constructed by this research institute for user-oriented topics with the TextAttBiRNN (text attention bi-directional recurrent neural network) single-hierarchical classification model and multi-hierarchical classification models, such as HFT-CNN (hierarchical fine-tuning conventional neural network) and HCCNN (hierarchical classification conventional neural network). The proposed model showed superior performance in realizing the multi-hierarchical single-label and ultra-fine-grained text information classification applications in the online Q&A communities.
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Received: 29 March 2021
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