Leveraging TF-IDF and Random Forest to Uncover Genre Patterns in Google Books Metadata - Putri, Nadya Awalia; Mukti, Bayu Priya (Bright Institute, 2025)
This paper presents a machine learning-based approach for classifying books into genres using their descriptions. We employed a Random Forest classifier combined with Term Frequency-Inverse Document Frequency (TF-IDF) to convert text descriptions into numerical features, enabling the classification of books into six genres: Fiction, Literary Criticism, Education, Social Science, Biography & Autobiography, and Unknown Genre. The model was trained and evaluated on a dataset sourced from Google Books, which was preprocessed to remove missing data and clean the text descriptions by eliminating punctuation, numbers, and stopwords. We performed 5-fold cross-validation to assess the model's performance, which resulted in an average cross-validation accuracy of 64.22%. The final model achieved an accuracy of 62.71% on the test set, with the highest recall observed in the