Data Analysis of Product Information and Reviews on E-commerce Platforms Based on Python

Data Analysis of Product Information and Reviews on E-commerce Platforms Based on Python | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Data Analysis of Product Information and Reviews on E-commerce Platforms Based on Python Chu Fang, Yunying Qin, Chi-Shun Liao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8493736/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract In the digital environment, e-commerce platforms have accumulated a large amount of both structured and unstructured data. As a core carrier of user perception, review content has a significant impact on consumer behavior and platform operations. To systematically mine such information, this study constructs a multi-level analytical framework. First, based on Python, multi-source review data is subjected to sentiment annotation and visual analysis to reveal basic distribution patterns. Furthermore, for the small-sample scenario, a hybrid 1-AGO-Prophet model is employed to explore the feasibility of trend forecasting for review volume; simultaneously, the XGBoost model is applied for fine-grained sentiment classification, with a focus on examining the impact of class imbalance issues. Experimental results show that the XGBoost model achieves high accuracy in recognizing positive sentiment but exhibits limitations in identifying sparse negative reviews. In contrast, the forecasts of the Prophet model exhibit considerable uncertainty due to the limited sample size. This study not only verifies the applicability of the relevant methods in e-commerce review analysis but also delves deeper into the challenges posed by data scarcity and class imbalance, providing a reference for platforms to intelligently manage reviews that balance methodological innovation with an awareness of limitations. Physical sciences/Engineering Physical sciences/Mathematics and computing Python e-Commerce reviews sentiment analysis trend modeling user behavior Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 09 Mar, 2026 Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers invited by journal 14 Jan, 2026 Editor assigned by journal 14 Jan, 2026 Editor invited by journal 13 Jan, 2026 Submission checks completed at journal 12 Jan, 2026 First submitted to journal 12 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8493736","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":575871920,"identity":"36270b9f-0d1c-4d83-81d4-a10ed9e6b9be","order_by":0,"name":"Chu 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