Name
Abstract | ||
|---|---|---|
Automatic book genre classification is a hard task as it requires the whole book's content or a high-quality summary, which is challenging to write automatically. On the other hand, online reviews are an accessible resource for readers to evaluate a book or even get a general sense about it, including its genre. As the amount of book reviews is always increasing, using such information to genre classification needs a robust solution to deal with high volumes of data. In such a context, we introduce a model for automatically classifying book genres by analyzing online text reviews. We build a dataset of compiled texts from online book reviews. Then, we use multiple machine learning algorithms to categorize a book into a specific genre. Such a process enables to compare algorithms and detect the best classifiers. Hence, the most efficient machine learning algorithm completed the task with an accuracy of 96%; i.e., the proposed model is convenient for various information retrieval systems due to its high certainty and efficiency. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-030-98305-5_18 | COMPUTATIONAL PROCESSING OF THE PORTUGUESE LANGUAGE, PROPOR 2022 |
Keywords | DocType | Volume |
Text classification, Book genre classification, Online reviews, Multiclass classification | Conference | 13208 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Clarisse Scofield | 1 | 0 | 0.34 |
| Mariana O. Silva | 2 | 0 | 1.01 |
| Luiza de Melo-Gomes | 3 | 0 | 0.34 |
| Mirella M. Moro | 4 | 0 | 0.34 |