Genetic Algorithm-Based Sentiment Analysis for Cyberbullying Detection

Royappa, Valarine Elizabeth (2023) Genetic Algorithm-Based Sentiment Analysis for Cyberbullying Detection. Masters thesis, Dublin, National College of Ireland.

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Abstract

In the era of social media and online platforms, cyberbullying has emerged as a significant social problem, adversely affecting victim’s mental well-being and online safety. This paper offers a thorough framework for improving sentiment analysis in the context of detecting cyberbullying. The study employs a rigorous experimental design to tackle the problem of recognizing both sentiment and possible cases of cyberbullying. It does this by utilizing a broad collection of Twitter data. The approach uses TF-IDF vectorization for classical machine learning and tokenization with embedding for deep learning models for data collection, pre-processing, and feature extraction. In addition to integrating logistic regression and SVM classifiers, the work investigates the construction of an LSTM model for sentiment analysis. Furthermore, feature selection is optimized for better model performance using a genetic algorithm-based method. To fully evaluate the models' effectiveness, evaluation measures such as accuracy, precision, recall, F1-score, and AUC-ROC are used. The findings highlight the methodology's potential to improve sentiment analysis and cyberbullying identification, advancing both scholarly inquiry and real-world applications.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Chikkankod, Arjun UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites > Online social networks T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites > Online social networks
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	28 Dec 2024 15:56
Last Modified:	28 Dec 2024 15:56
URI:	https://norma.ncirl.ie/id/eprint/7260

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