[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85019-en":3,"doc-seo-85019-105":29,"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":13,"seo_description":14,"update_tm":27,"read_time":28},85019,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Unveiling Public Opinion A Study of Sentiment Analysis Using LSTM and Traditional Models","Social media platforms such as Twitter function as real-time spaces where people express opinions and emotions across ongoing events. Sentiment analysis, an essential NLP capability, transforms large volumes of user-generated text into actionable insights by labeling tweets as positive, negative, or neutral and supporting public opinion interpretation and trend forecasting. This study compares logistic regression, random forest, naïve Bayes, gradient boosting, and LSTM on a Kaggle Twitter dataset, achieving strong results with LSTM (90.98% training, 80.00% testing, micro-average ROC-AUC 0.92) and demonstrating improved contextual, sequential understanding.","Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models  \nAtiq Ur Rehman  \nGraduate Student, Department of Economics and Decision Sciences, University of South Dakota, USA [atiq.ur.rehman.shafeeq@gmail.com](atiq.ur.rehman.shafeeq@gmail.com), [atiqur.rehman@usd.edu](atiqur.rehman@usd.edu)  \nAbstract—In this age of social media, sites like Twitter have become meeting places for people to share their views and feelingson a wide range of issues and current events as they unfold in realtime. Sentiment analysis, a critical application of NLP, has become indispensable due to the massive influx of user-generated content, enabling the extraction of meaningful insights from the opinions and emotions expressed in textual data. Sentiment analysis on Twitter employs sophisticated computational techniques to categorize tweets into positive, negative, or neutral sentiments. This method not only examines individual expressions but also analyzes vast databases related to specific subjects or events. By spotting these emotions, machine learning models help improve public opinion interpretation and trend forecasting. This paper examines the effectiveness of various machine learning and deep learning approaches. Designed for this use, the system evaluates logistic regression, random forest, naïve bayes, gradient boosting, and LSTM networks, among other algorithms applied in sentiment classification. This work identifies the optimal sentiment analysis model using a Kaggle Twitter dataset that has been preprocessed through tokenization, lemmatization, and stopword elimination. Emphasizing the better performance of the LSTM approach, the model attained a training accuracy of 90.98%, a testing accuracy of 80.00%, and a micro-average ROC-AUC score of 0.92. These results show that the model outperforms conventional machine learning techniques in capturing contextual and sequential textual aspects.  \nKeywords—Machine Learning, Sentiment Analysis, LSTM, Tweet Sentiment, Tokenization, Lemmatization.  \nI. INTRODUCTION  \nSentiment analysis is a crucial branch of natural language processing (NLP) that focuses on extracting and interpreting subjective opinions from text-based data [1] . Understanding sentiment offers valuable insights into consumer behavior, societal trends, and market dynamics, driven by the increasing reliance on digital platforms for communication [2] . Modern sentiment analysis uses multimodal methods to analyze textual, auditory, and visual data. This integration enhances analysis by providing a deeper understanding of human emotions and sentiments [3] . Social media makes more public opinions and sentiments available than ever. Sentiment analysis is crucial for comprehending public opinion in various fields, including business and politics [6] . Sentiment analysis is also used in health, social policy, e-commerce, and digital humanities. Privacy concerns and dataset biases must be addressed in each domain to deploy sentiment analysis tools ethically and effectively [4] .  \nAs shown in Figure 1, machine learning classifiers like logistic regression, naive Bayes, and support vector machines rely on statistical models to predict sentiment [6] . On the other hand, deep learning approaches, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, use artificial neural networks to analyze complex patterns in text for sentiment prediction [7] . Additionally, ensemble learning techniques combine various classifiers to improve the accuracy and effectiveness of sentiment analysis [5].  \nAnalyzing extensive social media comments to categorize products or services can be time-consuming [8] . Twitter has emerged as a widely used platform for expressing strong emotions, making it an ideal source for sentiment-related data, where users often share and discuss their opinions [9] . While machine learning algorithms can perform tasks quickly, they usually fall short in terms of performance","cbCainARMcAMgMu0","https://ap.wps.com/l/cbCainARMcAMgMu0","pdf",447569,1,6,"English","en",105,"# Introduction\n# Literature Review\n# Methodology\n## Model Comparison\n## Dataset Preprocessing\n# Results and Discussion\n## Performance Metrics\n# Conclusion","[{\"question\":\"What problem does the paper address in sentiment analysis?\",\"answer\":\"The paper addresses how to extract and interpret subjective opinions from large-scale Twitter text, and how to predict tweet sentiment for real-time analysis and public opinion understanding.\"},{\"question\":\"Which models are evaluated for sentiment classification?\",\"answer\":\"It evaluates logistic regression, random forest, naïve Bayes, gradient boosting, and LSTM networks, comparing their effectiveness for tweet sentiment categorization.\"},{\"question\":\"Why is the LSTM approach emphasized over traditional machine learning methods?\",\"answer\":\"The study reports that LSTM performs better because it captures contextual and sequential aspects of text, leading to higher overall effectiveness on the chosen dataset.\"}]",1784200339,15,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"unveiling-public-opinion-a-study-of-sentiment-analysis-using-lstm-and-traditional-models","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/unveiling-public-opinion-a-study-of-sentiment-analysis-using-lstm-and-traditional-models/85019/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in sentiment analysis?","Question",{"text":75,"@type":76},"The paper addresses how to extract and interpret subjective opinions from large-scale Twitter text, and how to predict tweet sentiment for real-time analysis and public opinion understanding.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which models are evaluated for sentiment classification?",{"text":80,"@type":76},"It evaluates logistic regression, random forest, naïve Bayes, gradient boosting, and LSTM networks, comparing their effectiveness for tweet sentiment categorization.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is the LSTM approach emphasized over traditional machine learning methods?",{"text":84,"@type":76},"The study reports that LSTM performs better because it captures contextual and sequential aspects of text, leading to higher overall effectiveness on the chosen 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