Comparative Analysis of Machine Learning Algorithms for Author Age and Gender Identification (original) (raw)

Abstract

Author profiling is part of information retrieval in which different perspectives of the author are observed by considering various characteristics like native language, gender, and age. Different techniques are used to extract the required information using text analysis, like author identification on social media and for Short Text Message Service. Author profiling helps in security and blogs for identification purposes while capturing authors’ writing behaviors through messages, posts, comments, blogs, comments, and chat logs. Most of the work in this area has been done in English and other native languages. On the other hand, Roman Urdu is also getting attention for the author profiling task, but it needs to convert Roman-Urdu to English to extract important features like Named Entity Recognition (NER) and other linguistic features. The conversion may lose important information while having limitations in converting one language to another language. This research explores machine learning techniques that can be used for all languages to overcome the conversion limitation. The Vector Space Model (VSM) and Query Likelihood (Q.L.) are used to identify the author’s age and gender. Experimental results revealed that Q.L. produces better results in terms of accuracy.

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Authors and Affiliations

  1. City University of Science and Information Technology, Peshawar, Pakistan
    Zarah Zainab
  2. College of Technological Innovation, Zayed University, Abu Dhabi, UAE
    Feras Al-Obeidat
  3. REMIT, IJP, Universidade Portucalense, Porto, Portugal
    Fernando Moreira
  4. IEETA, Universidade de Aveiro, Aveiro, Portugal
    Fernando Moreira
  5. Center for Excellence in Information Technology, Institute of Management Sciences, Peshawar, Pakistan
    Haji Gul & Adnan Amin

Authors

  1. Zarah Zainab
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  2. Feras Al-Obeidat
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  3. Fernando Moreira
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  4. Haji Gul
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  5. Adnan Amin
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Corresponding author

Correspondence toFernando Moreira .

Editor information

Editors and Affiliations

  1. Institute of Management Sciences, Peshawar, Pakistan
    Sajid Anwar
  2. School of Mathematical and Computer Science, Heriot-Watt University, Dubai, United Arab Emirates
    Abrar Ullah
  3. University of Lisbon, Lisbon, Portugal
    Álvaro Rocha
  4. University Institute of Lisbon (ISCTE), Lisbon, Portugal
    Maria José Sousa

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Zainab, Z., Al-Obeidat, F., Moreira, F., Gul, H., Amin, A. (2023). Comparative Analysis of Machine Learning Algorithms for Author Age and Gender Identification. In: Anwar, S., Ullah, A., Rocha, Á., Sousa, M.J. (eds) Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 614. Springer, Singapore. https://doi.org/10.1007/978-981-19-9331-2\_11

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