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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.21579 |
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| _version_ | 1866915514686636032 |
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| author | Khatun, Mst Eshita Akter, Halima Rehan, Tasnimul Ahmed, Toufiq |
| author_facet | Khatun, Mst Eshita Akter, Halima Rehan, Tasnimul Ahmed, Toufiq |
| contents | In this digital era, online shopping is common practice in our daily lives. Product reviews significantly influence consumer buying behavior and help establish buyer trust. However, the prevalence of fraudulent reviews undermines this trust by potentially misleading consumers and damaging the reputations of the sellers. This research addresses this pressing issue by employing advanced big data analytics and machine learning approaches on a substantial dataset of Amazon product reviews. The primary objective is to detect and classify spam reviews accurately so that it enhances the authenticity of the review. Using a scalable big data framework, we efficiently process and analyze a large scale of review data, extracting key features indicative of fraudulent behavior. Our study illustrates the utility of various machine learning classifiers in detecting spam reviews, with Logistic Regression achieving an accuracy of 90.35%, thus contributing to a more trustworthy and transparent online shopping environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21579 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging Big Data Frameworks for Spam Detection in Amazon Reviews Khatun, Mst Eshita Akter, Halima Rehan, Tasnimul Ahmed, Toufiq Machine Learning Computation and Language In this digital era, online shopping is common practice in our daily lives. Product reviews significantly influence consumer buying behavior and help establish buyer trust. However, the prevalence of fraudulent reviews undermines this trust by potentially misleading consumers and damaging the reputations of the sellers. This research addresses this pressing issue by employing advanced big data analytics and machine learning approaches on a substantial dataset of Amazon product reviews. The primary objective is to detect and classify spam reviews accurately so that it enhances the authenticity of the review. Using a scalable big data framework, we efficiently process and analyze a large scale of review data, extracting key features indicative of fraudulent behavior. Our study illustrates the utility of various machine learning classifiers in detecting spam reviews, with Logistic Regression achieving an accuracy of 90.35%, thus contributing to a more trustworthy and transparent online shopping environment. |
| title | Leveraging Big Data Frameworks for Spam Detection in Amazon Reviews |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2509.21579 |