[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121779-en":3,"doc-seo-121779-105":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":4,"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},121779,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Detecting Significant Behaviour in Tweets using Machine Learning - Research","Sentiment analysis plays a central role in computer science as social media expands and produces large volumes of short user comments, especially on Twitter. This study conducts an analytical investigation of tweets discussing the crisis in Pakistan, aiming to uncover public attitudes across different groups and influential figures. Machine learning models including Support Vector Classifier, Decision Tree, Naïve Bayes, and Logistic Regression are trained and compared to evaluate which approach best captures and predicts sentiment signals from the dataset.","Detecting Significant Behaviour in Tweets using  \nMachine Learning  \nFaisal Shahzad Faculty of Computing, The Islamia University of Bahawalpur.  \nBahawalpur, Pakistan.  \n[fs.ahmad65@gmail.com](fs.ahmad65@gmail.com)  \nMuhammad Asad Ullah Department of Information Technology, Faculty of Computing, The Islamia University of Bahawalpur.  \nBahawalpur, Pakistan  \nmuhammad.asadullah@iub  \n.[edu.pk](edu.pk)  \nMuhammad Adnan Khan Department of Computing, Skyline University College. Sharjah, United Arab Emirates.  \nmuhammad.adnan@skylin[euniversity.ac.ae](euniversity.ac.ae)  \nNouh Sabri Elmitwally School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, United Kingdom.  \n[nouh.elmitwally@bcu.ac.u](nouh.elmitwally@bcu.ac.u)k  \nAbstract—Sentiment Analysis is a crucial area of study within the realm of Computer Science. With the rapid advancement of Information Technology and the prevalence of social media, a substantial volume of textual comments has emerged on web platforms and social networks such as Twitter. Consequently, individuals have become increasingly active in disseminating both general and politically-related information, making it imperative to examine public responses. Many researchers have harnessed the unique features and content of social media to assess and forecast public sentiment regarding political events. This study presents an analytical investigation employing data from general discussions on Twitter to decipher public sentiment regarding the crisis in Pakistan. It involves the analysis of tweets authored by various ethnic groups and influential figures using Machine Learning techniques like the Support Vector Classifier (SVC), Decision Tree (DT), Naïve Bayes (NB) and Logistic Regression. Ultimately, a comparative assessment is conducted based on the outcomes obtained from different models in the experiments.  \nKeywords—hate speech, sentiment analysis, tweets, political opinion, insert.  \nI. INTRODUCTION  \n. The rapid expansion of online social networks (OSNs) has made communication platforms in high demand. This trend has facilitated broader data sharing, exploration, and information sharing, all without being limited by geographic boundaries (Antypas, Preece, and Camacho-Collados 2023) . The amount of content generated through social media channels, especially Twitter, is staggering. Twitter serves asan online environment for information and social interaction, where users communicate through short tweets (Lagman et al. 2018) . It has become the leading social media platform, with millions of users posting tweets regularly.  \nThe volume of public opinion data has increased exponentially(De Choudhury et al. 2016) . The ability to identify these perspectives on political events and issues is critical to shaping international agreements, policies and standards. Officials rely on these sentiments to inform their decisions, making it imperative that they closely monitor these data for future policy decisions(Davidson et al. 2017) .. Polls have traditionally served as the primary means of gathering public opinion, but they often present a number of  \nchallenges. These studies struggle to provide nuanced analysis or to reveal the underlying motivations, subjectivity, and intentions behind public sentiment. These limitations make opinion polls unreliable, highlighting the need for more sophisticated methods of understanding public opinion. The advent of social media, with its large user base, diverse topics, and large number of user-generated content, has emerged as an important tool for predicting human sentiment (Chung and Mustafaraj 2011) . Using advanced techniques toelicit political opinion on these platforms could provide a faster, more accurate, and more cost-effective alternative to traditional polls(Liu 2011) .  \nAlthough many studies have examined the potential of social media mining to analyze and predict political opinion, most of them have been event-oriented and used speci","cbCaisJj2fylQjp5","https://ap.wps.com/l/cbCaisJj2fylQjp5","pdf",455743,1,5,"English","en",105,"# Abstract\n# Introduction\n# Dataset\n## Data Collection\n## Data Preparation","[{\"question\":\"What is the document’s main goal?\",\"answer\":\"To detect and analyze significant public attitudes expressed in Twitter posts about the Pakistan crisis using machine learning models.\"},{\"question\":\"Which machine learning algorithms are used for the analysis?\",\"answer\":\"Support Vector Classifier (SVC), Decision Tree (DT), Naïve Bayes (NB), and Logistic Regression.\"},{\"question\":\"How are results evaluated in this study?\",\"answer\":\"A comparative assessment is performed based on the outcomes produced by different models in the experiments.\"}]","Detecting Significant Behaviour in Tweets using Machine Learning - Research | PDF",1785806800,13,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"detecting-significant-behaviour-in-tweets-using-machine-learning-research","",{"@graph":36,"@context":85},[37,54,68],{"@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/detecting-significant-behaviour-in-tweets-using-machine-learning-research/121779/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the document’s main goal?","Question",{"text":75,"@type":76},"To detect and analyze significant public attitudes expressed in Twitter posts about the Pakistan crisis using machine learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are used for the analysis?",{"text":80,"@type":76},"Support Vector Classifier (SVC), Decision Tree (DT), Naïve Bayes (NB), and Logistic Regression.",{"name":82,"@type":73,"acceptedAnswer":83},"How are results evaluated in this study?",{"text":84,"@type":76},"A comparative assessment is performed based on the outcomes produced by different models in the experiments.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]