Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Deep Learning, Computer Vision and Digital Image Processing
Juanda Pratama
Universitas Malikussaleh
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Integration of named entity recognition and latent Dirichlet allocation for extracting cyberbullying issues on X Juanda Pratama; Defry Hamdhana; Zara Yunizar
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1528

Abstract

Purpose – The rapid growth of social media Platform X has increased the risk of cyberbullying, which is difficult to detect due to the unstructured nature of textual data. This study proposes an integration of Named Entity Recognition (NER) and Latent Dirichlet Allocation (LDA) to support the extraction of cyberbullying-related social issues.Methods – A total of 2,000 tweets were processed through preprocessing, spaCy-based entity extraction, TF-IDF weighting, and LDA topic modeling. The latent topics generated by LDA were manually mapped into four predefined categories (Bodyshaming, Racism, Gender, and Neutral) and evaluated against researcher-annotated ground truth labels.Findings – Experimental results achieved an overall accuracy of 80%, with F1-scores of 94% for Racism, 93% for Gender, 70% for Bodyshaming, and 63% for Neutral.Research implications – The proposed framework provides practical support for monitoring cyberbullying patterns and assisting policymakers in understanding online social issues.Originality – The originality of this research lies in the sequential integration of NER as an entity-filtering stage prior to LDA, enabling a more comprehensive analysis of cyberbullying discussions than the isolated application of either method.
Co-Authors Hamdhana, Defry Zara Yunizar