Opinion Mining of Online Shopping Products Reviews Using Machine Learning

被引：0

作者：

Arra, Aashritha ^{[1
]}

Yeboah, Jones ^{[1
]}

Kofinti, Isaac ^{[1
]}

机构：

[1] Univ Cincinnati, Sch Informat Technol, Cincinnati, OH 45221 USA

来源：

2023 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE, CSCI 2023 | 2023年

关键词：

opinion milling; emotion classification; natural language processing; machine learning; regression;

D O I：

10.1109/CSCI62032.2023.00048

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the realm of electronic commerce, the impact of consumer sentiments on product selections is indisputable. This research introduces a method for harnessing machine learning's capabilities in analyzing sentiments within online product reviews. Given the exponential expansion of online shopping platforms, comprehending customer sentiments has become of utmost importance for enterprises striving to elevate product quality and customer contentment. Our approach encompasses various stages, including data gathering, data preprocessing, feature extraction, and the utilization of machine learning algorithms. Through extensive experimentation and assessment, we illustrate the effectiveness of our method in accurately categorizing sentiments expressed in product reviews. The investigation uncovers valuable insights into the critical determinants that shape customer viewpoints and highlights the potential for enterprises to utilize these insights in strategic decision-making processes. As the digital marketplace continues to evolve, our research offers an asset for enterprises seeking a competitive advantage. Our approach equips organizations to extract meaningful insights from vast repositories of online product reviews, ultimately facilitating well-informed product development and marketing strategies.

引用

页码：270 / 276

页数：7

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