[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117970-en":3,"doc-seo-117970-105":30,"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":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},117970,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Text Classification for Businesses via Microsoft Azure Machine Learning - Research Thesis","This thesis analyzes the “Corporate Messaging” dataset to classify different categories of business text using Microsoft Azure Machine Learning. The research details the end-to-end workflow, including data sourcing, acquisition, preparation, text preprocessing, and data architecture design. It evaluates multiple Azure ML Studio workflows and machine learning algorithms before and after feature selection and tuning, then applies hyperparameter tuning for accuracy. Model performance is assessed through metrics and testing on deployed models, leading to conclusions on the best-fit process and learning approach.","Text Classification for Businesses via Microsoft Azure Machine Learning  \nAnalysis on the ‘Corporate Messaging’ Dataset to Classify Different Categories of Text  \nby  \nFazrul Nazrin Bin Masrol  \nA thesis submitted to the graduate faculty  \nin partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nMajor: Information Systems  \nProgram of Study Committee:  \nDr. Anthony Townsend, Major Professor  \nThe student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this thesis. The Graduate College will ensure this thesis is globally accessible and will not permit alterations after a degree is conferred.  \nIowa State University  \nAmes, Iowa  \n2023  \nCopyright © Fazrul Nazrin Bin Masrol, 2023. All rights reserved.  \nTABLE OF CONTENTS  \nPage  \nLIST OF FIGURES ....................................................................................................................... iv  \nLIST OF TABLES ........................................................................................................................ vii  \nABSTRACT................................................................................................................................. viii  \nCHAPTER 1. INTRODUCTION ....................................................................................................1  \nMicrosoft Azure Component ..................................................................................................... 2  \nFundamental Methods ............................................................................................................... 2  \nCHAPTER 2. LITERATURE REVIEW .........................................................................................3  \nText Classification for Business Intelligence ............................................................................ 3  \nCorporate Messaging Dataset .................................................................................................... 5  \nCloud Based Machine Learning – Microsoft Azure .................................................................. 6  \nCHAPTER 3. RESEARCH METHODOLY .................................................................................10  \nData Source.............................................................................................................................. 10  \nData Acquisition ...................................................................................................................... 10  \nData Preparation ...................................................................................................................... 11  \nPreprocessing Text................................................................................................................... 11  \nData Architecture ..................................................................................................................... 11  \nCHAPTER 4. ANALYSES ...........................................................................................................15  \nSource Data Analyses .............................................................................................................. 15  \nText Clean-Up in Azure ML.................................................................................................... 15  \nAzure ML Studio Text-Preprocessing Steps: ..................................................................... 16  \n................................................................................................................................................. 19  \nML Algorithms in Azure ML Studio – Before Feature Selection and Tuning........................ 19  \nFilter Based Feature Selection in Azure ML ........................................................................... 20  \nML Algorithms After Feature Selections ..................................................................","cbCaio8QVr43SIvA","https://ap.wps.com/l/cbCaio8QVr43SIvA","pdf",2855717,1,61,"English","en",105,"# Chapter 1. Introduction\n## Microsoft Azure Component\n## Fundamental Methods\n# Chapter 2. Literature Review\n## Text Classification for Business Intelligence\n## Corporate Messaging Dataset\n## Cloud Based Machine Learning – Microsoft Azure\n# Chapter 3. Research Methodology\n## Data Source\n## Data Acquisition\n## Data Preparation\n## Preprocessing Text\n## Data Architecture\n# Chapter 4. Analyses\n## Source Data Analyses\n## Text Clean-Up in Azure ML\n## Azure ML Studio Text-Preprocessing Steps\n## ML Algorithms in Azure ML Studio – Before Feature Selection and Tuning\n## Filter Based Feature Selection in Azure ML\n## ML Algorithms After Feature Selections\n## Identifying Best Fit Process and Machine Learning Model\n## Predictions on Machine Learning Model\n## Testing the Deployed Machine Learning Model on a Dataset\n# Chapter 5. Observations\n## Metrics Evaluation BEFORE Feature Selection and Tuning\n## Metrics Evaluation AFTER Feature Selection and Tuning\n# Chapter 6. Hyperparameters for Tuning\n## Tuning Parameters for Different Machine Learning Algorithms\n## Tuning Parameters Best Identified for Accuracy\n## Tuning Parameters for Decision Tree\n## Tuning Parameters for K-Nearest Neighbors (KNN)\n## Tuning Parameters for Support Vector Machine (SVM)\n# Chapter 7. Model Evaluation and Conclusion\n# References","[{\"question\":\"What dataset and goal does the thesis focus on?\",\"answer\":\"The thesis analyzes the “Corporate Messaging” dataset to classify different categories of business text. The goal is to determine effective text classification approaches using Azure Machine Learning.\"},{\"question\":\"Which Azure ML workflow steps are included in the research?\",\"answer\":\"The study covers data acquisition and preparation, text preprocessing, data architecture, and Azure ML Studio text clean-up and preprocessing steps. It also evaluates algorithm runs before and after feature selection.\"},{\"question\":\"How are models tuned and evaluated in the thesis?\",\"answer\":\"Hyperparameters for several machine learning algorithms are tuned to improve accuracy. Model performance is evaluated using metrics both before and after feature selection and tuning, and the deployed model is tested on a dataset.\"}]","Text Classification for Businesses via Microsoft Azure Machine Learning - Research Thesis | PDF",1785680584,154,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"text-classification-for-businesses-via-microsoft-azure-machine-learning-research-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/text-classification-for-businesses-via-microsoft-azure-machine-learning-research-thesis/117970/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What dataset and goal does the thesis focus on?","Question",{"text":76,"@type":77},"The thesis analyzes the “Corporate Messaging” dataset to classify different categories of business text. The goal is to determine effective text classification approaches using Azure Machine Learning.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which Azure ML workflow steps are included in the research?",{"text":81,"@type":77},"The study covers data acquisition and preparation, text preprocessing, data architecture, and Azure ML Studio text clean-up and preprocessing steps. It also evaluates algorithm runs before and after feature selection.",{"name":83,"@type":74,"acceptedAnswer":84},"How are models tuned and evaluated in the thesis?",{"text":85,"@type":77},"Hyperparameters for several machine learning algorithms are tuned to improve accuracy. Model performance is evaluated using metrics both before and after feature selection and tuning, and the deployed model is tested on a dataset.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]