An Improved Dictionary Based Genre Classification Based on Title and Abstract of E-book Using Machine Learning Algorithms

The amount of digital books or e-books is increasing day by day. Book Assortment is the job of assigning a category or set of appropriate genres to a book. The goal of this research paper is to classify books with related genres. Many existing approaches, like Support Vector Machine (SVM), Neural Text Categorizer (NTC), etc. are available for text mining. We applied existing machine learning algorithms with different datasets and implemented existing feature selection methods to select features. In our proposed dictionary-based approach, we classified books by its attributes like title, description, genre, and author using text mining. In the learning part, we created a dictionary of keywords from the book’s description and title and then assigned genres to the keywords. In the classification part, we attributed genres to a book. For classifying the books, we extracted a dataset from web pages using web scraping. Our proposed approach outperforms traditional approaches to reduce the time of training when massive data is considered.