Book Genre Classification Based on Titles with Comparative Machine Learning Algorithms

Ozsarfati, Eran; Sahin, Egemen; Saul, Can Jozef; Yilmaz, Alper

doi:10.1109/ccoms.2019.8821643

Cited by 11 publications

(6 citation statements)

References 8 publications

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“…Genre classification occurs not only at the level of texts but also based on book titles. The authors of [17] presented a method for genre classification based on the book's title. The dataset (available at https://github.com/akshaybhatia10/Book-Genre-Classification, accessed on 1 January 2024) constructed by the authors contains 207,575 samples of data assigned to 32 different genres.To represent the data, the texts were converted to lowercase, tokenized, and stemmed.…”

Section: Related Workmentioning

confidence: 99%

Genre Classification of Books in Russian with Stylometric Features: A Case Study

Vanetik,

Tiamanova,

Kogan

et al. 2024

Information

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Within the literary domain, genres function as fundamental organizing concepts that provide readers, publishers, and academics with a unified framework. Genres are discrete categories that are distinguished by common stylistic, thematic, and structural components. They facilitate the categorization process and improve our understanding of a wide range of literary expressions. In this paper, we introduce a new dataset for genre classification of Russian books, covering 11 literary genres. We also perform dataset evaluation for the tasks of binary and multi-class genre identification. Through extensive experimentation and analysis, we explore the effectiveness of different text representations, including stylometric features, in genre classification. Our findings clarify the challenges present in classifying Russian literature by genre, revealing insights into the performance of different models across various genres. Furthermore, we address several research questions regarding the difficulty of multi-class classification compared to binary classification, and the impact of stylometric features on classification accuracy.

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Section: Related Workmentioning

confidence: 99%

Genre Classification of Books in Russian with Stylometric Features: A Case Study

Vanetik,

Tiamanova,

Kogan

et al. 2024

Information

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“…The pre-processed text is run through a parts of speech (POS) tagger, which assigns all of the words to their appropriate parts of speech. All the same characters but referred to by various names were connected together once the text was POS tagged [5], and all the interactions between those characters were identified. The character interaction graphs for the texts in the dataset are then created utilising the information provided above.…”

Section: Prior Workmentioning

confidence: 99%

A Comprehensive Machine Learning Framework for Automated Book Genre Classifier

Sethy¹,

Rout²,

Uriti³

et al. 2023

RIA

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Machine learning has been leveraged in the digital era, resulting in an increasing desire for computers to perform human-like tasks. Text classification is rapidly becoming one of the most significant applications of machine learning. However, the manual reading and classification of books based on genre requires substantial time and effort. As a result, machine learning methods are critical for enabling automated classification. In this study, a book description-based text classification framework was proposed, utilizing a wealth of information about book contents. The automated classification of books was achieved through the implementation of supervised machine learning. A variety of classifiers were employed, including Multinomial Naive Bayes, Gradient Boosting, and Random Forest, to categorize book genres. According to the results, the Naive Bayes classifier outperformed the other two techniques in classification accuracy, while comparable performance was achieved with Gradient Boosting and Random Forest. The comprehensive machine learning framework efficiently and accurately categorized books by extracting information from book descriptions. The proposed methodology has the potential to facilitate large-scale book classification for both academic and industrial purposes. Overall, this study provided an automated solution to relieve the burden of manual classification while achieving high accuracy.

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“…Ozsarfati, Sahin, Saul & Yilmaz [12] used a database developed by Akshay Bhatina [13], which is also based on the classification used by Iwana et al [11]. This database contains 207,575 samples of book titles divided into 32 genres.…”

Section: Related Workmentioning

confidence: 99%

“…Ozsarfati et al, [12], obtained 64.05% accuracy and 64.05% F1-score on a 32-genre book classification task using a LSTM model trained on book's titles. The task of classifying books into 32 categories have higher complexity than our task, but again two things to comment; first, our results are higher than this results, moreover our categories follow the THEME grouped in a way that is convenient for Latin American publishers.…”

Section: Comparison Of Means: Without Augmentation Vs With Back-trans...mentioning

confidence: 99%

Genre Classification of Books on Spanish

Nolazco-Flores,

Guerrero-Galván,

Del-Valle-Soto

et al. 2023

IEEE Access

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Genre categorization of published titles is a common practice in publishing houses, libraries, and bookstores, as well as a fundamental element of editorial marketing. However, assigning subject codes to each title proves to be an arduous task for both publishers and data aggregators. The problem with automatic genre categorization is that some publishers use more than 200 categories, making it a highly complex task. Moreover, even though these publishers based their categorization on standards, they ofthen alter the names of these standards as they consider to be too technical. In this paper, we proposed Themabased categorization as a tool to facilitate editors' work by advancing the categorization process, allowing them to focus on finer category granularity. This categorization has four key features: first, it clusters the most important categories for Latin American publishers. Second, it stops grouping when the number of thematic categories remains practical for the purposes of the publishing business. Third, we assign names to these categories that resonate with Latin American stakeholders. Finally, the number of categories is optimized to provide reasonable classification performance. We worked on the description of books in Spanish of two publishers, and mapped them to this proposed categorization. This allowed us to created a database for train a model to automate categorization. After conducting our analysis, we determined that 26 thematic categories were an appropriate number that fulfilled the three features mentioned earlier. However, we recognized that classifying into 26 categories was still a complex task, so to overcome this challenge, we decided to augment data by back-translating it into Spanish using the translation function, 𝑇 ! " "𝑇 " ! (𝑆)&, where 𝑇 " ! (𝑆) is the translation function from Spanish, 𝑠, to language, 𝑙; 𝑇 ! # (𝑙) is the translation function from language, 𝑙, to Spanish, 𝑆, and 𝑇 " ! (𝑆) and 𝑇 ! " (𝑙) are not-invertible functions. Experimental results, obtained using 5-fold cross-validation, were approximately 57%, 57%, 63.38%, and 65.26% for the F1-score of Support Vector Machine (SVM), Logist Regression (LR), BERT, and RoBERTa models, respectively. We utilized the F1-score metric because our categories were not perfectly balanced. The results achieved by RoBERTa outperform those reported in the literature. Furthermore, these results are built upon the foundation of the Thema standard for categorizing book genres. Additionally, the categories have been specifically designed to align with the preferences and needs of Latin American publishers.INDEX TERMS Genre categorization; text classification; book categorization; BERT; RoBERTa.

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Book Genre Classification Based on Titles with Comparative Machine Learning Algorithms

Cited by 11 publications

References 8 publications

Genre Classification of Books in Russian with Stylometric Features: A Case Study

Genre Classification of Books in Russian with Stylometric Features: A Case Study

A Comprehensive Machine Learning Framework for Automated Book Genre Classifier

Genre Classification of Books on Spanish

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