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Traditional sentiment analysis struggles with informal language, contextual nuances, and aspect-specific opinions. This paper proposes a hybrid sentiment framework combining lexicon-based VADER, machine learning (SVM and Random Forest), and deep learning (BERT), improved by preprocessing such as emoji normalization, negation handling, and intensifier detection. Aspect-based sentiment analysis (ABSA) is used to evaluate service components including driver behaviour, app performance, and pricing, validated on Uber reviews with strong accuracy.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/hybrid-sentiment-analysis-model-for-customer-feedback-interpretation-using-lexicon-machine-learning-and-deep-learning-techniques/291227/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/hybrid-sentiment-analysis-model-for-customer-feedback-interpretation-using-lexicon-machine-learning-and-deep-learning-techniques/291227.png","ImageObject",300,407,{"name":92,"@type":93},"Ophelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",24,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why do traditional sentiment analysis methods underperform for customer feedback?","Question",{"text":113,"@type":114},"They often fail with informal language, contextual nuances, and aspect-specific opinions, making them less suitable for large-scale or real-time analysis.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What models and techniques does the proposed hybrid framework use?",{"text":118,"@type":114},"It combines VADER (lexicon-based), Support Vector Machine and Random Forest (machine learning), and BERT (deep learning), along with preprocessing steps such as emoji normalization, negation handling, and intensifier detection.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the study evaluate performance and what results are reported?",{"text":122,"@type":114},"Experiments on the Uber Customer Reviews Dataset assess accuracy, macro F1-score, and interpretability. The hybrid model reports 99% accuracy and a macro F1-score of 0.98, outperforming individual models.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291227,1789643807,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","Journal of Informatics and Web Engineering  \nVol. 5 No.1 (February 2026) eISSN: 2821-370X  \nHybrid Sentiment Analysis Model for Customer Feedback Interpretation Using Lexicon, Machine Learning and Deep Learning  \nTechniques  \nJou Jia Yi1, Lew Sook Ling2, Tan Li Tao3*,  \n1Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Bukit Beruang, Melaka, Malaysia.  \n2Centre for Advanced Analytics, CoE for Artificial Intelligence, Jalan Ayer Keroh Lama, 75450 Bukit Beruang, Melaka, Malaysia.  \n3Faculty of Law, The Sir David Williams Building, 10 West Road, Cambridge CB3 9DZ, United Kingdom  \n*corresponding author: ([ltt28@cam.ac.uk](ltt28@cam.ac.uk); ORCiD: 0009-0004-7133-3045)  \nAbstract-Customer feedback is pivotal in enhancing service quality and user satisfaction across digital platforms. However, traditional sentiment analysis methods often struggle with informal languages, contextual nuances, and aspect-specific opinions. In this paper, a hybrid sentiment analysis framework is proposed, utilizing lexicon-based (Valence Aware Dictionary and sEntiment Reasoner (VADER)), machine learning (Support Vector Machine and Random Forest), and deep learning (BERT) techniques to achieve improved sentiment classification accuracy and interpretability compared to previous studies. The framework incorporates advanced preprocessing techniques, such as emoji normalization, handling of negation, and detection of intensifiers, to better capture emotional information in user-generated content. The objectives of this study are to develop a robust sentiment analysis system that can accurately classify user sentiment and extract aspect-specific insights from customer feedback. Aspect-based Sentiment Analysis (ABSA) was also employed to provide detailed evaluations of specific service components, including driver behaviour, app performance, and pricing. In this study, experimental results using the Uber Customer Reviews Dataset demonstrate that the proposed hybrid model achieves 99% accuracy, significantly outperforms the individual model, and obtains a macro F1-score of 0.98. These findings confirm that integrating lexicon-based, machine learning, and deep learning approaches enhances sentiment classification effectiveness and supports data-driven decision making based on user experience.  \nKeywords—Aspect-Based Sentiment Analysis, Bidirectional Encoder Representations from Transformers, Customer Feedback, Natural Language Processing, Sentiment Analysis, Support Vector Machines, Uber, Valence Aware Dictionary and sEntiment Reasoner  \nReceived: 17 May 2025; Accepted: 7 August 2025; Published: 16 February 2026 This is an open access article under the CC BY-NC-ND 4.0 license.  \n1. INTRODUCTION  \nIn today’s data-driven service-oriented economy, customer feedback is extremely valuable to organizations that want to improve service quality, user satisfaction, and organizational efficiency. There are a multitude of available user opinions about Internet services in the form of online reviews, comments on app stores, and postings on social media. When these feedback, sources are analysed appropriately, they can yield insights that can be acted upon [1], [2] . However, traditional feedback analysis methods are often manual, time-consuming, and prone to subjective bias, rendering them unsuitable for large-scale or real-time applications [3] .  \nHybrid sentiment analysis is a multipronged approach that incorporates lexicon-based methods (VADER), machine learning models (Support Vector Machine and Random Forest) [4], and deep learning (BERT), leveraging the advantages of both machine learning and human effort. In addition, when producing the output, the Aspect-based Sentiment Analysis (ABSA) is utilized to can uncover sentiment about different components of service, such as driver behaviours, value of app(s), and pricing [5], [6] . To address these limitations, a hybrid sentiment analysis framework with a mix of methodolo","cbCaitdS3d2W2xjG","https://ap.wps.com/l/cbCaitdS3d2W2xjG","pdf",843118,17,"English","# Abstract\n# 1. Introduction\n# 2. Literature Review","[{\"question\":\"Why do traditional sentiment analysis methods underperform for customer feedback?\",\"answer\":\"They often fail with informal language, contextual nuances, and aspect-specific opinions, making them less suitable for large-scale or real-time analysis.\"},{\"question\":\"What models and techniques does the proposed hybrid framework use?\",\"answer\":\"It combines VADER (lexicon-based), Support Vector Machine and Random Forest (machine learning), and BERT (deep learning), along with preprocessing steps such as emoji normalization, negation handling, and intensifier detection.\"},{\"question\":\"How does the study evaluate performance and what results are reported?\",\"answer\":\"Experiments on the Uber Customer Reviews Dataset assess accuracy, macro F1-score, and interpretability. The hybrid model reports 99% accuracy and a macro F1-score of 0.98, outperforming individual models.\"}]","Hybrid Sentiment Analysis Model for Customer Feedback Interpretation Using Lexicon, Machine Learning and Deep Learning Techniques | PDF",43]