A Survey on Book Genre Classification System using Machine Learning

Abstract

The amount of complicated texts and documents that need a deeper understanding of machine learning techniques has expanded rapidly in recent decades. Several machine learning approaches have shown exceptional results in NLP. Complex models and non-linear data correlations help these learning systems function well. It is difficult for academics to discover appropriate text classification structures, designs, and procedures. Manually reading and classifying a book's category would be tedious. It takes a long time for a language beginner to absorb the complete text in order to discern its genre. Natural Language Processing (NLP), which is commonly used nowadays, can help overcome this issue. It includes text classification and summarization. The book synopsis is input to the Machine Learning algorithms, which output the genre. This review includes text feature extraction, dimensionality reduction, algorithms, and evaluation. Finally, the limitations of each strategy are assessed, as well as their practicality.