标题： 基于改进连续再归一化过程的互联网评论主题层次结构挖掘方法 显示英文标题

标题： An internet reviews topic hierarchy mining method based on modified continuous renormalization procedure

摘要： 挖掘互联网评论主题的层次结构并实现评论文本的细粒度分类，有助于缓解用户的的信息过载。 然而，现有的层次主题分类方法主要依赖外部语料库和人工干预。 本研究提出了一种改进的连续再归一化（MCR）过程，该过程作用于具有分形特征的关键词共现网络，以实现主题层次结构的挖掘。 首先，首次使用盒覆盖算法识别互联网评论文本关键词共现网络中的分形特征。 然后，基于边邻接熵和盒距离提出的MCR算法用于在关键词共现网络中获得主题层次结构。 来自当当网书籍评论的验证数据表明，与HLDA和Louvain算法相比，MCR构建的主题层次结构具有更高的连贯性和独立性。 最后，通过扩展MCR底部层次的主题类别实现了可靠的评论文本分类。 基于MCR的主题层次结构获得的互联网评论文本分类的准确率（P）、召回率（R）和F1值相比四种目标文本分类算法有显著提高。 显示英文摘要

摘要： Mining the hierarchical structure of Internet review topics and realizing a fine classification of review texts can help alleviate users' information overload. However, existing hierarchical topic classification methods primarily rely on external corpora and human intervention. This study proposes a Modified Continuous Renormalization (MCR) procedure that acts on the keyword co-occurrence network with fractal characteristics to achieve the topic hierarchy mining. First, the fractal characteristics in the keyword co-occurrence network of Internet review text are identified using a box-covering algorithm for the first time. Then, the MCR algorithm established on the edge adjacency entropy and the box distance is proposed to obtain the topic hierarchy in the keyword co-occurrence network. Verification data from the Dangdang.com book reviews shows that the MCR constructs topic hierarchies with greater coherence and independence than the HLDA and the Louvain algorithms. Finally, reliable review text classification is achieved using the MCR extended bottom level topic categories. The accuracy rate (P), recall rate (R) and F1 value of Internet review text classification obtained from the MCR-based topic hierarchy are significantly improved compared to four target text classification algorithms.