  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.
