[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120286-en":3,"doc-seo-120286-105":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},120286,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",6,"Technology","Understanding the relevance of parallelising machine learning algorithms using CUDA for sentiment analysis","Sentiment classification is a core capability in natural language processing, translating textual signals into measurable emotions for decision-making. Building on machine learning approaches such as Naive Bayes and Support Vector Machines, the work leverages word embeddings like Word2Vec and GloVe to represent semantic and syntactic relationships. It evaluates GPU-accelerated parallelisation with CUDA to improve computational efficiency, comparing CPU versus GPU execution time and model accuracy. Experiments show Naive Bayes delivers stronger runtime efficiency and higher accuracy, precision, and F1, supported by GPU acceleration across different thread configurations for scalable sentiment analysis.","Understanding the relevance of parallelising machine learning algorithms using CUDA for sentiment analysis  \nDakun Mang Chai∗  \nDepartment of Computational Engineering Sciences Cranfield University Bedford, Bedfordshire, United Kingdom [dakun.chai@cranfield.ac.uk](dakun.chai@cranfield.ac.uk)  \nIrene Moulitsas  \nDepartment of Computational Engineering Sciences Cranfield University Bedford, Bedfordshire, United Kingdom [i.moulitsas@cranfield.ac.uk](i.moulitsas@cranfield.ac.uk)  \nDesmond Bala Bisandu  \nDepartment of Computational Engineering Sciences Cranfield University Bedford, Bedfordshire, United Kingdom  \n[desmond.bisandu@cranfield.ac.uk](desmond.bisandu@cranfield.ac.uk)  \nAbstract  \nSentiment classification is essential in natural language processing, leveraging machine learning algorithms to understand the sentiment expressed in textual data. Over the years, advancements in machine learning, particularly with Naive Bayes (NB) and Support Vector Machines (SVM), have tremendously improved sentiment classification. These models benefit from word embedding techniques such as Word2Vec and GloVe, which provide dense vector representations of words, capturing their semantic and syntactic relationships. This paper explores the parallelisation of NB and SVM models using CUDA on GPUs to enhance computational efficiency and performance. Despite the computational power offered by GPUs, the literature on parallelising machine learning methods, especially for sentiment classification, remains limited. Our work aims to fill this gap by comparing the performance of NB and SVMon CPU and GPU platforms, focusing on execution time and model accuracy. Our experiments demonstrate that NB outperforms SVMin execution time and overall efficiency, mainly when using GPU acceleration. The NB model consistently achieves higher accuracy, precision, and F1 scores with Word2Vec and GloVe embeddings. The results show the importance of leveraging GPU acceleration using varying numbers of threads per block for large-scale sentiment analysis and laying the foundation for parallelising sentiment classification tasks.  \nCCS Concepts  \n• Computing methodologies → Information extraction; Featureselection; Cross-validation; Parallel algorithms.  \nKeywords  \nSentiment Analysis, CUDA, Machine Learning, Word Embedding  \nACM Reference Format:  \nDakun Mang Chai, Irene Moulitsas, and Desmond Bala Bisandu. 2024. Understanding the relevance of parallelising machine learning algorithms  \n∗ corresponding author.  \nThis work is licensed under a Creative Commons Attribution International 4 .0 License.  \nICAAI 2024, London, United Kingdom  \n© 2024 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-1801-4/24/10  \n[https://doi.org/10.1145/3704137.3704142](https://doi.org/10.1145/3704137.3704142)  \nusing CUDA for sentiment analysis. In 2024 The 8th International Conference on Advances in Artificial Intelligence (ICAAI 2024), October 17–19, 2024, London, United Kingdom. ACM, New York, NY, USA, 11 pages. [https:](https:)//[doi.org/10.1145/3704137.3704142](doi.org/10.1145/3704137.3704142)  \n1 Introduction  \nIn the era of big data, sentiment analysis has emerged as a tool for understanding public opinion and extracting insights from large volumes of textual data. Sentiment analysis (SA), a subset of natural language processing (NLP), involves using machine learning algorithms to classify the tone in text data [1], [2], [3] . This capability is crucial for businesses, governments, and researchers who aim to gauge public sentiment, improve customer satisfaction, and make informed decisions. The exponential growth of unstructured data generated online drives the need for sentiment analysis. With millions of new posts and reviews appearing daily, manually analysing this data is impractical. Therefore, automated sentiment analysis using NLP techniques is essential for efficiently processing and interpreting these vast datasets. NLP allows machines to understand and process hum","cbCaipv46ZpTuhJ5","https://ap.wps.com/l/cbCaipv46ZpTuhJ5","pdf",628718,1,11,"English","en",105,"# Abstract\n# CCS Concepts and Keywords\n# Introduction\n## Sentiment analysis in the big data era\n## Computational challenges and GPU suitability\n## CUDA and GPU-based parallelisation","[{\"question\":\"What is the main focus of this paper?\",\"answer\":\"The paper focuses on parallelising Naive Bayes and SVM for sentiment analysis using CUDA on GPUs, comparing CPU and GPU performance in execution time and accuracy.\"},{\"question\":\"Why are GPUs and CUDA used for sentiment analysis in this work?\",\"answer\":\"GPUs enable parallel computation through many smaller cores, and CUDA provides a platform to exploit this parallelism to accelerate preprocessing, feature extraction, and model training.\"},{\"question\":\"Which model performs better in the experiments and why?\",\"answer\":\"Naive Bayes performs better than SVM, especially in execution time and overall efficiency, and it achieves higher accuracy, precision, and F1 scores when using Word2Vec and GloVe embeddings.\"}]","Understanding the relevance of parallelising machine learning algorithms using CUDA for sentiment analysis | PDF",1785729247,28,{"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},"understanding-the-relevance-of-parallelising-machine-learning-algorithms-using-cuda-for-sentiment-analysis","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/understanding-the-relevance-of-parallelising-machine-learning-algorithms-using-cuda-for-sentiment-analysis/120286/",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","2026-08-03",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 main focus of this paper?","Question",{"text":75,"@type":76},"The paper focuses on parallelising Naive Bayes and SVM for sentiment analysis using CUDA on GPUs, comparing CPU and GPU performance in execution time and accuracy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are GPUs and CUDA used for sentiment analysis in this work?",{"text":80,"@type":76},"GPUs enable parallel computation through many smaller cores, and CUDA provides a platform to exploit this parallelism to accelerate preprocessing, feature extraction, and model training.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs better in the experiments and why?",{"text":84,"@type":76},"Naive Bayes performs better than SVM, especially in execution time and overall efficiency, and it achieves higher accuracy, precision, and F1 scores when using Word2Vec and GloVe embeddings.","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},[93,97,101,105,110,113,118,123,128,131,135],{"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":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]