Analysis of the Use of Methods and Feature Groups for Author Recognition on the Example of Texts in the Azerbaijani Language

Azimov, Rustam

doi:10.1109/pci60110.2023.10325956

2023 5th International Conference on Problems of Cybernetics and Informatics (PCI) 2023

DOI: 10.1109/pci60110.2023.10325956

|View full text |Cite

Analysis of the Use of Methods and Feature Groups for Author Recognition on the Example of Texts in the Azerbaijani Language

Rustam Azimov

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Authorship Attribution for English Short Texts

Alsanoosy,

Shalbi,

Noor

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Internet and social media explosive growth has led to the rapid and widespread dissemination of information, which often takes place anonymously. This anonymity has fostered the rise of uncredited copying, posing a significant threat of copyright infringement and raising serious concerns in fields where verifying information's authenticity is paramount. Authorship Attribution (AA), a critical classification task within Natural Language Processing (NLP), aims to mitigate these concerns by identifying the original source of content. Although extensive research exists for longer texts, AA for short texts, namely informal texts like tweets, remains challenging due to the latter’s brevity and stylistic variation. Thus, this study aims to investigate and measure the performance of various Machine Learning (ML) and Deep Learning (DL) methods deployed for feature extraction from short text data, using tweets. The employed feature extraction methods were: Bag-of-Words (BoW), TF-IDF, n-grams, word-level, and character-level features. These methods were evaluated in conjunction with six ML classifiers, i.e. Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Random Forest (RF) along with two DL architectures, i.e. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The highest accuracy achieved with an ML model was 92.34%, using an SVM with TF-IDF features. Even though the basic CNN DL model reached 88% accuracy, this outcome still surpassed the previously established baseline for this task. The findings of this research not only advance the technical capabilities of AA, but also extend its practical applications, providing tools that can be adapted across various domains to ensure proper attribution and expose copyright infringement.

show abstract

Authorship Attribution for English Short Texts

Alsanoosy,

Shalbi,

Noor

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Analysis of the Use of Methods and Feature Groups for Author Recognition on the Example of Texts in the Azerbaijani Language

Cited by 1 publication

References 6 publications

Authorship Attribution for English Short Texts

Authorship Attribution for English Short Texts

Contact Info

Product

Resources

About