Using Machine Learning to identify hate speech and offensive language on Twitter - BSc (Hons) in Computing in IT

Aug 4, 2026

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The dissertation applies machine learning to detect hate speech and offensive language in Twitter data, addressing both data quality and classification performance. It outlines the end-to-end workflow from business and data understanding through processing, preparation, modelling, evaluation, and deployment, supported by structured analysis steps. Ethical and legal aspects are considered alongside a CRISP-DM based methodology. Results include dataset inspection, cleaning decisions, feature and class handling, and visual/text exploration to support model development and reporting.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages39

Reading Time1 h 38 min

AccessFree to download

Summary

Tools

Abstract

Introduction

Literature Review

Ethical and Legal Aspects

Methodology

Business Understanding

Data Understanding

Data Processing

Data Preparation

Modelling

Evaluation

Deployment

Individual Report Thayene

Individual Report Mayara

Final Considerations

References

Index

CCT College Dublin ARC (Academic Research Collection) | BSc (Hons) in Computing in IT | ICT ETD Collections | | --- | --- | | 5-2024
Using Machine Learning to identify hate speech language on Twitter.
Mayara Lorens CCT College Dublin
Thayene Lorens CCT College Dublin
Follow this and additional works at: [https://arc.cct.ie/bs_honscompit](https://arc.cct.ie/bs_honscompit)
Part of the Computer Sciences Commons | and offensive | Recommended Citation Lorens, Mayara and Lorens, Thayene, "Using Machine Learning to identify hate speech and offensive language on Twitter." (2024) . BSc (Hons) in Computing in IT. 1. [https://arc.cct.ie/bs_honscompit/1](https://arc.cct.ie/bs_honscompit/1) This Dissertation is brought to you for free and open access by the ICT ETD Collections at ARC (Academic Research Collection) . It has been accepted for inclusion in BSc (Hons) in Computing in IT by an authorized administrator of ARC (Academic Research Collection) . For more information, please [contact debora@cct.ie](contact debora@cct.ie). | Module Title: | Problem Solving for Industry | | --- | --- | | Assessment Title: | Capstone Pair Project | | Lecturer Name: | Dr. Muhammad Iqbal | | Student Full Name: | Mayara Lorens; Thayene Lorens | | Student Number: | 2020292, 2020293 | | Assessment Due Date: | 17/05/2024 | | Date of Submission: | 22/05/2024 | Declaration By submitting this assessment, I confirm that I have read the CCT policy on Academic Misconduct and understand the implications of submitting work that is not my own or does not appropriately reference material taken from a third party or other source. I declare it tobe my own work and that all material from third parties has been appropriately referenced. I further confirm that this work has not previously been submitted for assessment by myself or someone else in CCT College Dublin or any other higher education institution. Summary Tools ............................................................................................................................. 7 Abstract ...................................................................................................................................... 8 Introduction .................................................................................................................... 9 Literature Review ........................................................................................................ 10 Ethical and Legal Aspects............................................................................................. 11 Methodology .................................................................................................................. 12 1. Business Understanding....................................................................................... 12 Data Understanding .................................................................................................. 13 Data Processing ........................................................................................................21 Data Preparation ....................................................................................................22 Modelling..................................................................................................................24 Evaluation ....................................................................................................................27 Deployment .................................................................................................................... 31 Individual Report Thayene ......................................................................................................32 Individual Report Mayara ........................................................................................................33 Final Considerations ...........................................................................................

FAQs

What problem does the dissertation address?

It addresses identifying hate speech and offensive language in Twitter content using machine learning methods.

What methodology does the work follow?

The workflow follows a structured process consistent with CRISP-DM, covering business understanding, data understanding, processing, preparation, modelling, evaluation, and deployment.

How is the dataset handled during the study?

The study performs dataset inspection and cleaning steps, including checking missing values and duplicates, transforming/labeling classes, and exploring text characteristics such as tweet length and class distribution.

Aug 4, 2026

Upload by Hazel