American International Journal of Research and Innovation

E-ISSN: XXXX-XXXX   •   Impact Factor: -

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 5 (September-October 2026) Submit your research before last 3 days of October to publish your research paper in the issue of September-October.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 5
September-October 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Cyberbullying Detection Through Natural Language Processing Techniques

Author(s) Marius Portmann
Country United States
Abstract The rapid expansion of digital communication platforms has transformed social interaction patterns while simultaneously increasing exposure to harmful online behaviours such as cyberbullying. Traditional approaches for identifying abusive digital communication often depend on manual monitoring, which is time-consuming, subjective, and difficult to apply at large scales. Natural Language Processing (NLP) techniques provide computational approaches for analysing textual information, identifying linguistic patterns, and supporting automated cyberbullying detection systems. This study explores the application of NLP-based approaches for detecting cyberbullying patterns in online textual communication.
A simulation-based analytical framework is developed to examine the effectiveness of machine learning-oriented text classification, sentiment analysis, feature extraction, and contextual language analysis methods.The proposed framework evaluates linguistic indicators associated with harmful communication, including offensive expressions, emotional intensity, repeated aggression, and contextual hostility.
The findings indicate that NLP-driven models can significantly improve early identification of cyberbullying risks by combining linguistic features with contextual interpretation. However, challenges related to sarcasm detection, multilingual communication, privacy protection, and ethical algorithmic decision-making remain significant areas requiring further investigation. The study contributes to understanding how artificial intelligence-based language analysis can support safer digital environments while maintaining fairness, transparency, and responsible technology governance.
Keywords Cyberbullying Detection, Natural Language Processing, Machine Learning, Text Classification, Sentiment Analysis, Artificial Intelligence, Digital Safety
Field Engineering
Published In Volume 1, Issue 5, September-October 2019
Published On 2019-09-03
View / Download PDF File

Share this



E-ISSN XXXX-XXXX
CrossRef

CrossRef DOI prefix of AIJRI is 10.00000/AIJRI

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About AIJRI
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +1-872-302-3851 editor@aijri.com