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.
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Notes
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Domínio Público: https://www.dominiopublico.gov.br.
- 2.
Projecto Adamastor: https://projectoadamastor.org.
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- 4.
On Goodreads, a bookshelf is a list where one can add or remove books to facilitate reading, similar to a real-life bookshelf where one keeps books.
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- 6.
TF-IDF is a numerical statistic that reflects how important a word is to a document within a collection or corpus. Its value increases proportionally to the number of times a word appears in the document and is offset by the number of documents that contain it (source: wikipedia).
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As implemented by sklearn.dummy.DummyClassifier using prior as strategy.
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Scikit Learn: https://scikit-learn.org/stable.
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Standard metrics for evaluating models, common in Information Retrieval. Precision = truePositive/predictedPositive. Recall = truePositive/totalActualPositive. Accuracy = \((truePositive+trueNegative)/(Positive+Negative)\). F1 = \(2\times ((Precision\times Recall)/(Precision+Recall))\).
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This work was funded by CNPq and FAPEMIG, Brazil.
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Scofield, C., Silva, M.O., de Melo-Gomes, L., Moro, M.M. (2022). Book Genre Classification Based on Reviews of Portuguese-Language Literature. In: Pinheiro, V., et al. Computational Processing of the Portuguese Language. PROPOR 2022. Lecture Notes in Computer Science(), vol 13208. Springer, Cham. https://doi.org/10.1007/978-3-030-98305-5_18
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