[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127106-en":3,"doc-seo-127106-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127106,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Using Machine Learning to Identify Hate Speech and Offending Language on Twitter - Undergraduate Project","The project applies machine learning to detect hate speech and other offensive language in Twitter content, aiming to support safer online communication. The study defines business understanding, prepares and processes tweet data, and builds classification models following a structured workflow. Evaluation and deployment activities assess performance and outline how the solution can be used, while ethical and legal considerations guide responsible handling of user-generated text. Figures illustrate dataset behavior and the CRISP-DM approach.","CCT College Dublin  \nARC (Academic Research Collection)  \nICT  \n2024  \nUsing Machine Learning to Identify Hate Speech and Offending Language on Twitter.  \nMayara Lorens CCT College Dublin  \nThayene Lorens CCT College Dublin  \nFollow this and additional works at: [https://arc.cct.ie/ict](https://arc.cct.ie/ict)  \n Part of the Computer Sciences Commons, and the Data Science Commons  \nRecommended Citation  \nLorens, Mayara and Lorens, Thayene, \"Using Machine Learning to Identify Hate Speech and Offending Language on Twitter.\" (2024) . ICT. 70.  \n[https://arc.cct.ie/ict/70](https://arc.cct.ie/ict/70)  \nThis Undergraduate Project is brought to you for free and open access by ARC (Academic Research Collection) . It has been accepted for inclusion in ICT by an authorized administrator of ARC (Academic Research Collection) . For more information, please [contact](contact debora@cct.ie)[ debora@cct.ie](contact debora@cct.ie).  \n| Module Title: | Problem Solving for Industry |\n| --- | --- |\n| Assessment Title: | Using Machine Learning to Identify Hate Speech and Offending Language on Twitter |\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 ................................................................................................................ 34  \n References ...............................","cbCaigfy9r9LAiN9","https://ap.wps.com/l/cbCaigfy9r9LAiN9","pdf",1659362,2,1,39,"English","en",105,"# Summary\n## Tools\n## Abstract\n## Introduction\n## Literature Review\n## Ethical and Legal Aspects\n## Methodology\n## References\n## Index","[{\"question\":\"What is the project about?\",\"answer\":\"The project focuses on using machine learning to identify hate speech and offending language in Twitter posts.\"},{\"question\":\"How is the work structured from understanding to deployment?\",\"answer\":\"It follows a defined methodology including business understanding, data understanding and processing, modelling, evaluation, and deployment.\"},{\"question\":\"What considerations are included beyond technical steps?\",\"answer\":\"The project includes ethical and legal aspects, addressing responsible handling of user-generated content and academic conduct.\"}]","Using Machine Learning to Identify Hate Speech and Offending Language on Twitter - 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