Research on Book Topic Planning Based on Online Review Mining
DOI:
https://doi.org/10.63313/EBM.9178Keywords:
Online Reviews, Book Topic Planning, Text Mining, Literary PublishingAbstract
In the context of digital reading and the growing importance of online reviews for publishing decisions, traditional topic planning faces challenges such as information overload and delayed feedback. This study analyzes over 59,000 valid reviews from Dangdang.com’s TOP15 best-selling literary books (April 2025). Using a “text mining + multi-dimensional comparison” approach and an optimized DTC framework with a topic mapping layer, we identify 17 core features of literary books via TF-IDF and K-means. Findings show that keywords like “classic,” “moving,” and “healing” significantly influence topic decisions and can be directly translated into content, author, and format strategies. The study validates the DTC model’s applicability in publishing and provides data-driven support for topic planning.
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