This paper is published in Volume-8, Issue-1, 2022
Area
Electronics and Telecommunication
Author
Aishwary Suhas Shivarkar, Vaishali Bagade
Org/Univ
Alamuri Ratnamala Institute of Engineering and Technology, Shahapur, Maharashtra, India
Pub. Date
01 February, 2022
Paper ID
V8I1-1311
Publisher
Keywords
A Cyberbullying Detection Method, Machine Learning, The Naive Bayes, KNN, Decision Tree, Random Forest, and Support Vector Machine Techniques

Citationsacebook

IEEE
Aishwary Suhas Shivarkar, Vaishali Bagade. A study of cyberbullying detection using Machine Learning techniques, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Aishwary Suhas Shivarkar, Vaishali Bagade (2022). A study of cyberbullying detection using Machine Learning techniques. International Journal of Advance Research, Ideas and Innovations in Technology, 8(1) www.IJARIIT.com.

MLA
Aishwary Suhas Shivarkar, Vaishali Bagade. "A study of cyberbullying detection using Machine Learning techniques." International Journal of Advance Research, Ideas and Innovations in Technology 8.1 (2022). www.IJARIIT.com.

Abstract

With the widespread use of online social networks and their popularity, social networking platforms have provided us with innumerable chances not previously available, and their benefits are evident. Regardless of blessings, people can be embarrassed, ridiculed, tormented, and compelled to utilize the resource of anonymous users, strangers, or peers. In this study, we offered a cyberbullying detection method to build abilities from Twitter content material by employing a pointwise mutual facts technique. We superior a supervised tool learning solutions for cyberbullying detection and multi-beauty classification of its severity in Twitter based on the one's talents. We achieved Embedding, Sentiment, and Lexicon characteristics, as well as PMI-semantic orientation, throughout the test. The extraction of features was conducted utilizing