[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121935-en":3,"doc-seo-121935-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121935,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Using Machine Learning to identify hate speech and offensive language on Twitter - BSc (Hons) in Computing in IT","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.","CCT College Dublin  \nARC (Academic Research Collection)  \n\n| BSc (Hons) in Computing in IT | ICT ETD Collections |\n| --- | --- |\n| 5-2024\u003Cbr>Using Machine Learning to identify hate speech language on Twitter.\u003Cbr>Mayara Lorens CCT College Dublin\u003Cbr>Thayene Lorens CCT College Dublin\u003Cbr>Follow this and additional works at: [https://arc.cct.ie/bs_honscompit](https://arc.cct.ie/bs_honscompit)\u003Cbr> Part of the Computer Sciences Commons | and offensive |\n\nRecommended Citation  \nLorens, Mayara and Lorens, Thayene, \"Using Machine Learning to identify hate speech and offensive language on Twitter.\" (2024) . BSc (Hons) in Computing in IT. 1.  \n[https://arc.cct.ie/bs_honscompit/1](https://arc.cct.ie/bs_honscompit/1)  \nThis 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).  \n| Module Title: | Problem Solving for Industry |\n| --- | --- |\n| Assessment Title: | Capstone Pair Project |\n| Lecturer Name: | Dr. Muhammad Iqbal |\n| Student Full Name: | Mayara Lorens; Thayene Lorens |\n| Student Number: | 2020292, 2020293 |\n| Assessment Due Date: | 17/05/2024 |\n| Date of Submission: | 22/05/2024 |\n\nDeclaration  \nBy 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.  \nSummary  \nTools ............................................................................................................................. 7  \nAbstract ...................................................................................................................................... 8  \nIntroduction .................................................................................................................... 9  \nLiterature Review ........................................................................................................ 10  \nEthical and Legal Aspects............................................................................................. 11  \nMethodology .................................................................................................................. 12  \n1. Business Understanding....................................................................................... 12  \nData Understanding .................................................................................................. 13  \nData Processing ........................................................................................................21  \nData Preparation ....................................................................................................22  \nModelling..................................................................................................................24  \nEvaluation ....................................................................................................................27  \nDeployment .................................................................................................................... 31  \nIndividual Report Thayene ......................................................................................................32  \nIndividual Report Mayara ........................................................................................................33  \nFinal Considerations ...........................................................................................","cbCaip5qd2q9dVhf","https://ap.wps.com/l/cbCaip5qd2q9dVhf","pdf",1657389,1,39,"English","en",105,"# Summary\n## Tools\n## Abstract\n## Introduction\n## Literature Review\n## Ethical and Legal Aspects\n## Methodology\n## Business Understanding\n## Data Understanding\n## Data Processing\n## Data Preparation\n## Modelling\n## Evaluation\n## Deployment\n## Individual Report Thayene\n## Individual Report Mayara\n## Final Considerations\n## References\n## Index","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It addresses identifying hate speech and offensive language in Twitter content using machine learning methods.\"},{\"question\":\"What methodology does the work follow?\",\"answer\":\"The workflow follows a structured process consistent with CRISP-DM, covering business understanding, data understanding, processing, preparation, modelling, evaluation, and deployment.\"},{\"question\":\"How is the dataset handled during the study?\",\"answer\":\"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.\"}]","Using Machine Learning to identify hate speech and offensive language on Twitter - 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