[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127196-en":3,"doc-seo-127196-105":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},127196,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Sentiment Analysis of Product Reviews for Ecommerce Recommendation based on Machine Learning","In today’s competitive business environment, sentiment analysis is vital for improving brand performance and customer decision-making. Leveraging natural language processing (NLP) and machine learning, this study explains how companies can monitor public opinion across social media, reviews, and online forums. It analyzes feedback from competing brands to detect trends, evaluate brand perception, and support proactive responses. The paper reviews lexicon-based and deep learning techniques, discusses cases impacting product innovation, marketing, and crisis management, and summarizes key challenges such as sarcasm, multilingual content, and real-time processing for data-driven intelligence.","International Journal of Innovative Research in Science  \nEngineering and Technology (IJIRSET)  \n(A Monthly, Peer Reviewed, Refereed, Scholarly Indexed, Open Access Journal)  \nImpact Factor: 8.699 Volume 14, Issue 5 , May 2025  \n|  |\n| --- |\n|  |\n| |[www.ijirset.com](www.ijirset.com |A Monthly)[ |A Monthly](www.ijirset.com |A Monthly), Peer Reviewed & Refereed Journal| e-ISSN: 2319-8753| p-ISSN: 2347-6710|\u003Cbr>Volume 14, Issue 5, May 2025\u003Cbr>|DOI: 10.15680/IJIRSET.2025.1405039|\u003Cbr>Sentiment Analysis of Product Reviews for Ecommerce Recommendation based on\u003Cbr>Machine Learning\u003Cbr>S.Siva Ranjani, S.Kalaichelvi\u003Cbr>II MCA, Department of Computer Science and Applications, Vivekanandha College of Arts and Sciences for Women\u003Cbr>(Autonomous), Tiruchengode, Tamilnadu, India\u003Cbr>Assistant Professor, Department of Computer Science and Applications, Vivekanandha College of Arts and Sciences\u003Cbr>for Women (Autonomous), Tiruchengode, Tamilnadu, India\u003Cbr>ABSTRACT: In today ’s competitive business landscape, understanding consumer sentiment is crucial for brand success. Sentiment analysis, powered by natural language processing (NLP) and machine learning, enables companies to monitor public opinion across social media, reviews, and online forums. This paper explores how sentiment analysis can be leveraged for competitive brand monitoring, helping businesses identify market opportunities and threats. By analyzing customer feedback on competing brands, companies can detect emerging trends, measure brand perception, and respond proactively to concerns We discuss various sentiment analysis techniques, including lexicon-based and deep learning approaches, and their effectiveness in extracting actionable insights. Additionally, we highlight case studies where sentiment analysis has influenced strategic decisions, such as product innovation, marketing campaigns, and crisis management. The study concludes with challenges in sentiment analysis, including handling sarcasm, multilingual data, and real-time processing. By integrating sentiment analysis into their competitive intelligence strategy, brands can enhance their market positioning and drive data-driven decision-making.\u003Cbr>KEYWORDS: Brand Perception, Lexicon-Based Sentiment Analysis, Competitive Intelligence, Product Innovation, Crisis Management.\u003Cbr>I. INTRODUCTION\u003Cbr>In today's digital era, brands are constantly discussed across various online platforms such as social media, review sites, and forums. Sentiment analysis, a branch of natural language processing (NLP), plays a crucial role in understanding customer opinions, emotions, and attitudes towards products and services. For competitive brand monitoring, sentiment analysis helps companies gain insights into public perception, enabling them to identify market opportunities and potential threats. By analyzing large volumes of user-generated content, businesses can detect emerging trends, customer preferences, and areas for improvement. Positive sentiments highlight strengths and successful strategies, while negative sentiments reveal weaknesses and challenges that need to be addressed. Furthermore, sentiment analysis aids in tracking competitor performance, allowing companies to refine their strategies and stay ahead in the market. This paper focuses on how sentiment analysis can be effectively utilized for competitive brand monitoring, with an emphasis on identifying market opportunities and mitigating threats. It explores various techniques, tools, and methodologies used in sentiment analysis and highlights its importance in making data-driven business decisions. Sentiment analysis has become a pivotal tool in competitive brand monitoring, enabling businesses togauge public opinion, identify market trends, and make informed strategic decisions. By analyzing consumer sentiments expressed across various platforms, companies can uncover both opportunities and threats in the marketplace.Real-Time Brand Health Tracking:Sentiment analysis allows co","cbCaiff6qj9QWawP","https://ap.wps.com/l/cbCaiff6qj9QWawP","pdf",1182862,1,9,"English","en",105,"# Abstract\n# Introduction\n# Literature Survey","[{\"question\":\"How does sentiment analysis support competitive brand monitoring in ecommerce?\",\"answer\":\"It analyzes large volumes of user-generated content to infer public perception, identify market opportunities, and detect potential threats by tracking positive and negative sentiments over time.\"},{\"question\":\"What sentiment analysis techniques are discussed in the paper?\",\"answer\":\"The paper covers lexicon-based approaches and deep learning methods, explaining how each can extract actionable insights from customer feedback.\"},{\"question\":\"What challenges does sentiment analysis face according to the study?\",\"answer\":\"Key challenges include handling sarcasm, processing multilingual data, and enabling real-time sentiment analysis for timely decision-making.\"}]","Sentiment Analysis of Product Reviews for Ecommerce Recommendation based on Machine Learning | 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does sentiment analysis support competitive brand monitoring in ecommerce?","Question",{"text":75,"@type":76},"It analyzes large volumes of user-generated content to infer public perception, identify market opportunities, and detect potential threats by tracking positive and negative sentiments over time.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What sentiment analysis techniques are discussed in the paper?",{"text":80,"@type":76},"The paper covers lexicon-based approaches and deep learning methods, explaining how each can extract actionable insights from customer feedback.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges does sentiment analysis face according to the study?",{"text":84,"@type":76},"Key challenges include handling sarcasm, processing multilingual data, and enabling real-time sentiment analysis for timely 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