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Sentiment Analysis of Bangladeshi E-Commerce Site Reviews Using Machine Learning Approaches

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Sentiment Analysis of Bangladeshi E-Commerce Site Reviews Using Machine Learning Approaches


Mohammad Kasedullah | Nakib Aman | Md. Mehedi Hasan



Mohammad Kasedullah | Nakib Aman | Md. Mehedi Hasan "Sentiment Analysis of Bangladeshi E-Commerce Site Reviews Using Machine Learning Approaches" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-3, June 2024, pp.442-448, URL: https://www.ijtsrd.com/papers/ijtsrd64875.pdf

In the context of Bangladesh, the E-commerce sector is experiencing continuous growth, particularly during the global crisis. Amidst the plethora of available platforms, Daraz has emerged as the most successful marketplace, offering users a wide array of shopping options. However, the abundance of reviews and comments on this online platform presents a challenge for consumers trying to make optimal choices. This research focuses on systematically categorizing positive and negative reviews to enhance user decision-making. To achieve this objective, a range of classifiers, including Multinomial Naive Bayes, Logistic Regression, Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, and Support Vector Machine with different kernels, were employed. The dataset underwent thorough cleaning, followed by the application of Term Frequency-Inverse Document Frequency (TF-IDF) with Principal Component Analysis (PCA) to enhance feature representation. The findings of this study indicate that the Multinomial Naive Bayes classifier, especially when utilizing Bigram and Trigram features, outperformed other classifiers, demonstrating superior accuracy. The implementation of this classifier holds significant promise for assisting businesses operating on various platforms, enabling them to distinguish between positive and negative reviews effectively. Consequently, this approach empowers businesses to furnish customers with valuable insights into the quality of products, contributing to a more informed and confident consumer base.

E-commerce, Daraz, Reviews, Sentiment Analysis, Multinomial Naive Bayes, Logistic Regression, Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, Support Vector Machine, Data Cleaning, Term Frequency-Inverse Document Frequency (TF-IDF), Principal Component Analysis (PCA), Product Quality, User Decision-Making


IJTSRD64875
Volume-8 | Issue-3, June 2024
442-448
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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