E-commerce Product Price Monitoring and Comparison using Sentiment Analysis
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Abstract
The increasing prevalence of e-commerce has empowered consumers with vast choices and opportunities for online shopping. This research paper focuses on two essential aspects of online shopping: price comparison and sentiment analysis of product reviews. The paper presents a methodology for scraping product prices from multiple e-commerce websites and conducting sentiment analysis on the corresponding product reviews. The findings of this research have significant implications for both consumers and e-commerce businesses. Consumers can leverage price comparison data to identify the most cost-effective platforms for their desired products, while sentiment analysis enables them to assess the overall satisfaction levels of other customers. E-commerce businesses can utilize these insights to optimize pricing strategies, identify areas for improvement, and enhance customer experiences. Performance analysis of Support Vector Machine, Logistic Regression, VADER Lexicon and SentiWordNet Lexicon is also done.
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References
Nougarahiya, Shrey and Shetty, Gaurav and Mandloi, Dheeraj, A Review of E – Commerce in India: The Past, Present, and the Future (March 15, 2021). Research Review International Journal of Multidisciplinary. Volume 06 Issue 03, March 2021, 12-22, Available at SSRN: https://ssrn.com/abstract=3809521
Ikechukwu Onyenwe, Ebele Onyedinma, Chidinma Nwafor and Obinna Agbata "Developing Products Update-Alert System for e-Commerce Websites Users Using HTML Data and Web Scraping Technique ", IJNLC Vol-10, Issue-10, 2021 https://doi.org/10.48550/arXiv.2109.00656
Fang, X., Zhan, J. Sentiment analysis using product review data. Journal of Big Data 2, 5 (2015). https://doi.org/10.1186/s40537-015-0015-2
Daroch, B., Nagrath, G. and Gupta, A. (2021), "A study on factors limiting online shopping behaviour of consumers", Rajagiri Management Journal, Vol. 15 No. 1, pp. 39-52. https://doi.org/10.1108/RAMJ-07-2020-0038
Moaiad Ahmad Khder, "Web Scraping or Web Crawling: State of Art, Techniques, Approaches and Application", Int. J. Advance Soft Compu. Appl, Vol. 13, No. 3, November 2021 Print ISSN: 2710-1274
Susan M. Mudambi, "Mudambi & Schuff/Consumer Reviews on Amazon.com RESEARCH NOTEWHAT MAKES A HELPFUL ONLINE REVIEW? A STUDY OF CUSTOMER REVIEWS ON AMAZON.COM", MIS Quarterly Vol. 34 No. 1, pp. 185-200/March 2010
NN Sakhare, SA Joshi, “Criminal Identification System Based On Data Mining” 3rd ICRTET, ISBN, Issue 978-93, Pages 5107-220, 2015
NN Sakhare, SA Joshi, “Classification of criminal data using J48-Decision Tree algorithm” IFRSA International Journal of Data Warehousing & Mining, Vol. 4, 2014
NN Sakhare, SS Imambi, Technical Analysis Based Prediction of Stock Market Trading Strategies Using Deep Learning and Machine Learning Algorithms, International Journal of Intelligent Systems and Applications in Engineering, 2022, 10(3), pp. 411–42.
Sakhare,N.N., Shaik,I.S.,Saha,S.: Prediction of stock market movement via technical analysis of stock data stored on blockchain using novel History Bits based machine learning algorithm. IET Soft.1–12(2023). https://doi.org/10.1049/sfw2.1209212
NN Sakhare, SS Imambi, S Kagad, H Malekar, M Dalal, “Stock market prediction using sentiment analysis” International Journal of Advanced Science and Technology, Vol. 4, issue 3, 2020.