The Internet is strengthening the e-commerce industry, which is fast growing and helping enterprises of all sizes, from multinational organizations to tiny firms. Customers may buy things online with little or no personal interaction with sellers they purchase online; user reviews play a vital role in online shopping. Consumers’ comprehension and interpretation of product reviews impacts buying decisions. This research paperwork presents a unique, reproducible data processing methodology for customer evaluations across 10 product categories on India’s one of the most popular e-commerce platforms with 11,559 customer reviews. We investigated the efficacy of a collection of machine learning algorithms that may be used to assess huge reviews on e-commerce platforms by using consumer ratings as a source to automatically classify product reviews as highly trustable or not-so-trustable. Results show that the algorithms can reach up to 85% of accuracy in classifying product reviews correctly. The research discusses the practical ramifications of these findings in terms of consumer complaints and product returns, as evidenced by customer reviews.
|Title of host publication||3rd Congress on Intelligent Systems - Proceedings of CIS 2022|
|Editors||Sandeep Kumar, Harish Sharma, K. Balachandran, Joong Hoon Kim, Jagdish Chand Bansal|
|Number of pages||9|
|Publication status||Published - 2023|
|Name||Lecture Notes in Networks and Systems|
Bibliographical notePublisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.