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Author Identification from Literary Articles with Visual Features: A Case Study with Bangla Documents

Author

Listed:
  • Ankita Dhar

    (Department of Computational Science, Brainware University, Kolkata 700125, India)

  • Himadri Mukherjee

    (Department of Computer Science, West Bengal State University, Kolkata 700126, India)

  • Shibaprasad Sen

    (Techno Main Saltlake, Kolkata 700091, India)

  • Md Obaidullah Sk

    (Department of Computer Science and Engineering, Aliah University, Kolkata 700156, India)

  • Amitabha Biswas

    (Department of Computer Science, West Bengal State University, Kolkata 700126, India)

  • Teresa Gonçalves

    (Department of Computer Science, University of Évora, 7000-671 Évora, Portugal
    ALGORITMI Research Center, Vista Lab, University of Évora, 7000-671 Évora, Portugal)

  • Kaushik Roy

    (Department of Computer Science, West Bengal State University, Kolkata 700126, India)

Abstract

Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as criminal and civil cases, cybersecurity, forensics, identification of plagiarizer, and many more. An automated system in this context can thus be very beneficial for society. In this paper, we propose a convolutional neural network (CNN)-based author identification system from literary articles. This system uses visual features along with a five-layer convolutional neural network for the identification of authors. The prime motivation behind this approach was the feasibility to identify distinct writing styles through a visualization of the writing patterns. Experiments were performed on 1200 articles from 50 authors achieving a maximum accuracy of 93.58%. Furthermore, to see how the system performed on different volumes of data, the experiments were performed on partitions of the dataset. The system outperformed standard handcrafted feature-based techniques as well as established works on publicly available datasets.

Suggested Citation

  • Ankita Dhar & Himadri Mukherjee & Shibaprasad Sen & Md Obaidullah Sk & Amitabha Biswas & Teresa Gonçalves & Kaushik Roy, 2022. "Author Identification from Literary Articles with Visual Features: A Case Study with Bangla Documents," Future Internet, MDPI, vol. 14(10), pages 1-20, September.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:10:p:272-:d:923732
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    References listed on IDEAS

    as
    1. Andi Rexha & Mark Kröll & Hermann Ziak & Roman Kern, 2018. "Authorship identification of documents with high content similarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 223-237, April.
    2. Moshe Koppel & Jonathan Schler & Shlomo Argamon, 2009. "Computational methods in authorship attribution," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(1), pages 9-26, January.
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