Biological gender identification in Turkish news text using deep learning models

Identifying the biological gender of authors based on the content of their written work is a crucial task in Natural Language Processing (NLP). Accurate biological gender identification finds numerous applications in fields such as linguistics, sociology, and marketing. However, achieving high accur...

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Bibliographic Details
Published in:	Multimedia tools and applications Vol. 83; no. 17; pp. 50669 - 50689
Main Authors:	Tüfekci, Pınar, Bektaş Kösesoy, Melike
Format:	Journal Article
Language:	English
Published:	New York Springer US 01-05-2024 Springer Nature B.V
Subjects:	Accuracy Algorithms Artificial neural networks Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Deep learning Gender Identification Linguistics Machine learning Multimedia Information Systems Natural language processing Neural networks Short term memory Sociolinguistics Sociology Special Purpose and Application-Based Systems Texts Turkish language Turkish News Text Biological Gender Identification Text Classification Machine Learning Deep Learning
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Summary:	Identifying the biological gender of authors based on the content of their written work is a crucial task in Natural Language Processing (NLP). Accurate biological gender identification finds numerous applications in fields such as linguistics, sociology, and marketing. However, achieving high accuracy in identifying the biological gender of the author is heavily dependent on the quality of the collected data and its proper splitting. Therefore, determining the best-performing model necessitates experimental evaluation. This study aimed to develop and evaluate four learning algorithms for biological gender identification in news texts. To this end, a comprehensive dataset, IAG-TNKU, was created from a Turkish newspaper, comprising 43,292 news articles. Four models utilizing popular machine learning algorithms, including Naive Bayes and Random Forest, and two deep learning algorithms, Long Short Term Memory and Convolutional Neural Networks, were developed and evaluated rigorously. The results indicated that the Long Short Term Memory (LSTM) algorithm outperformed the other three models, exhibiting an exceptional accuracy of 88.51%. This model's outstanding performance underpins the importance of utilizing innovative deep learning algorithms for biological gender identification tasks in NLP. The present study contributes to extant literature by developing a new dataset for biological gender identification in news texts and evaluating four machine learning algorithms. Our findings highlight the significance of utilizing innovative techniques for biological gender identification tasks. The dataset and deep learning algorithm can be applied in many areas such as sociolinguistics, marketing research, and journalism, where the identification of biological gender in written content plays a pivotal role.
ISSN:	1573-7721 1380-7501 1573-7721
DOI:	10.1007/s11042-023-17622-w