[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125829-en":3,"doc-seo-125829-105":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},125829,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Advancements in Machine Learning for Robust Sentiment Analysis of Consumer Product Reviews","Social media and large-scale e-commerce data have made sentiment analysis a core capability for understanding customer feelings from product reviews. This study simulates and evaluates an enhanced machine learning pipeline for review sentiment classification, combining state-of-the-art feature extraction, sentiment classification algorithms, and model optimization. It reviews prior work, details the improved strategy and design rationale, and tests performance on multiple product-review datasets. Experiments report accuracy, precision, recall, and F1-score, compare against baseline and cutting-edge systems, and analyze robustness across items and review types.","Advancements in Machine Learning for Robust Sentiment Analysis of Consumer Product Reviews  \nPawan Kumar1, Dr. Mukesh Kumar2  \n1Research Scholar, Department ofCSE, Rabindra Nath Tagore University, Bhopal, India  \n2Associate Professor, Department ofCSE, Rabindra Nath Tagore University, Bhopal, India  \nAbstract— : In this age of social media and huge data, sentiment analysis has become an essential activity. In order for businesses to measure consumer happiness and make educated decisions, it is crucial to understand the feelings conveyed in product reviews. The purpose of this study is to simulate and evaluate an enhanced machine learning approach to product review sentiment analysis. The goal is to create a powerful model for sentiment analysis that can beat current methods in terms of efficiency and accuracy. In this paper, we present a new approach to sentiment analysis in product reviews by integrating state-of-the-art feature extraction with sentiment classification algorithms and model optimization techniques. We begin by outlining the significance of sentiment analysis and the difficulties encountered by current approaches. Additionally, it specifies the aims and parameters of this study. The section on similar studies provides a thorough analysis of the literature and draws attention to the shortcomings of previous methods. In the methodology part, we lay out the specifics of our improved machine learning strategy and the thinking behind the methods we chose. In the results analysis, we test how well our model does on a variety of product review datasets. We compare our results to those of baseline models and cutting-edge sentiment analysis systems, and we provide the accuracy, precision, recall, and F1-score measures. Our discussion also covers the model's ability to handle different kinds of items and reviews.  \nIn comparison to more conventional approaches, our study shows that sentiment analysis is far more accurate. To demonstrate the model's efficacy in various contexts and to highlight its flaws, we use tables and graphs. At the end of the study, we go over some of the possible business uses, suggestions for further studies, and consequences of our results. In sum, this study aids in the development of sentiment analysis methods and gives a great resource for companies who want to learn more about how customers feel about their products through reviews..  \nKeywords-Product Review, Sentiment Analysis, Tweets, Machine Learning, Natural Language Processing, Deep Learning, Ensemble Learning  \nI. INTRODUCTION  \nAs we enter a new era of ubiquitous digital connection, the internet has profoundly altered human behavior in many areas, including social interaction, communication, and consumer choice. Online forums, social media, and e-commerce sites have given customers a voice they never had before when it comes to reviewing and discussing the goods and services they buy. Buyers now rely heavily on product reviews to influence their purchasing decisions and build opinions about products and brands. Businesses have come to realize the need of tracking and analyzing user sentiment in order to change their strategy, make customers happier, and stay ahead of the competition.  \nOpinion mining and sentiment analysis are two NLP techniques that automate the process of identifying the positive, negative, or neutral sentiment represented in a text. When it comes to product evaluations, sentiment analysis is crucial for gleaning useful information from large volumes of unstructured text. Businesses may learn a lot about their customers'experiences, problems, and wants by gauging the general opinion of a product or its features. This information can then be used to make data-driven choices on how to enhance their offerings.  \nThe rise of online reviews and social media interactions has greatly increased the importance of sentiment analysis in today's commercial world. Before making a purchase, consumers are more and more dependent o","cbCaicb21cJg6FNA","https://ap.wps.com/l/cbCaicb21cJg6FNA","pdf",374099,1,6,"English","en",105,"# Introduction\n## Sentiment analysis in consumer choice\n## Opinion mining and sentiment classification\n## Lexicon-based and machine learning approaches\n## Deep learning and transformer models\n# Methodology and Results Overview\n## Improved machine learning strategy\n## Dataset evaluation and metrics\n## Comparison with baselines\n# Discussion and Conclusion\n## Business applications and further work","[{\"question\":\"Why is sentiment analysis important for consumer product reviews?\",\"answer\":\"Product reviews and social media discussions strongly influence purchasing decisions. Businesses use sentiment signals to understand customer experiences and to make data-driven improvements.\"},{\"question\":\"What limitations do lexicon-based sentiment analysis methods have?\",\"answer\":\"Lexicon-based techniques are fast and simple but often miss language subtleties, context, and sarcasm, leading to inaccurate sentiment classification.\"},{\"question\":\"How does this study evaluate the proposed machine learning approach?\",\"answer\":\"The paper tests the enhanced model on multiple product review datasets and compares results with baseline and advanced systems using accuracy, precision, recall, and F1-score.\"}]","Advancements in Machine Learning for Robust Sentiment Analysis of Consumer Product Reviews | PDF",1785901449,15,{"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},"advancements-in-machine-learning-for-robust-sentiment-analysis-of-consumer-product-reviews","",{"@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/advancements-in-machine-learning-for-robust-sentiment-analysis-of-consumer-product-reviews/125829/",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","2026-08-05",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},"Why is sentiment analysis important for consumer product reviews?","Question",{"text":75,"@type":76},"Product reviews and social media discussions strongly influence purchasing decisions. Businesses use sentiment signals to understand customer experiences and to make data-driven improvements.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations do lexicon-based sentiment analysis methods have?",{"text":80,"@type":76},"Lexicon-based techniques are fast and simple but often miss language subtleties, context, and sarcasm, leading to inaccurate sentiment classification.",{"name":82,"@type":73,"acceptedAnswer":83},"How does this study evaluate the proposed machine learning approach?",{"text":84,"@type":76},"The paper tests the enhanced model on multiple product review datasets and compares results with baseline and advanced systems using accuracy, precision, recall, and F1-score.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"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":106,"slug":137},19,"General","general"]