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Mining the hierarchical structure of Internet review topics and realizing afine classification of review texts can help alleviate users' informationoverload. However, existing hierarchical topic classification methods primarilyrely on external corpora and human intervention. This study proposes a ModifiedContinuous Renormalization (MCR) procedure that acts on the keywordco-occurrence network with fractal characteristics to achieve the topichierarchy mining. First, the fractal characteristics in the keywordco-occurrence network of Internet review text are identified using abox-covering algorithm for the first time. Then, the MCR algorithm establishedon the edge adjacency entropy and the box distance is proposed to obtain thetopic hierarchy in the keyword co-occurrence network. Verification data fromthe Dangdang.com book reviews shows that the MCR constructs topic hierarchieswith greater coherence and independence than the HLDA and the Louvainalgorithms. Finally, reliable review text classification is achieved using theMCR extended bottom level topic categories. The accuracy rate (P), recall rate(R) and F1 value of Internet review text classification obtained from theMCR-based topic hierarchy are significantly improved compared to four targettext classification algorithms.

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