基于TF-PIDF的网络问答社区中的知识供需研究

Analyzing Knowledge Demand and Supply of Community Question Answering with TF-PIDF

【目的】为掌握民众的知识需求以及社区知识供给情况,进而有针对性地干预,构建网络问答社区中的知识需求和知识供应分析方法。【方法】针对问答对中的问题和答案均是多部分组成特点,构造了新的词权重计算方法 TF-PIDF,分别对问题和答案建模。通过对问题和答案分别聚类获得知识需求和知识供给的主要类别,获得各类别的主题以及热度。针对各知识需求类别,发现相应知识供给的主要方面。提出了知识需求覆盖度算法,计算知识需求被知识供给覆盖程度,在此基础上提出对知识需求从热度和覆盖度进行交叉分析。【结果】以知乎社区中的流感话题为实际案例进行应用研究,分别获得知识需求和知识供应的6个主题类别,其中热点主题均为"疫情",但其知识供应覆盖度较低,是突发流感事件下的热门实时知识需求。实验结果表明该方法合理可行。【局限】提出的分析框架和方法尽管能够有效地挖掘网络问答社区中知识需求和知识供应的主题,但识别出的主题主要是在特征词聚类所表达的主题含义层面上。【结论】本方法不仅能够获得民众的知识需求和社区的知识供给的情况,还能为知识补给以及社区运营提供重要依据。

[Objective] This paper propose a new method to study the knowledge demand and supply of community question answering, aiming to make effective targeted interventions. [Methods] First, we constructed novel word weight calculation models(TF-PIDF) for the questions and answers. Then, we obtained the main categories of demanded and supplied knowledge by clustering questions and answers, as well as the popularity of topics. Third, we paired the categories of knowledge demand and their supply counterparts. Fourth, we proposed an algorithm to calculate the popularity of knowledge demands. [Results] The proposed model was examined with topis on influenza from the community of ZHIHU. We found six categories of topics for knowledge demand and supply. The trending one was"epidemic", which represented the most popular real time needs. [Limitations]The identified topics rely on the topic meaning from feature word clustering. [Conclusions] The proposed method could effectively manage the knowledge demand and supply of community question answering.