Analyzing the Performance of Improved Random Forest Based Amazon Product Review Sentiment Analysis
Authors: 
Ashwini Bari, Rajeev Yadav, Satish Kumar Alaria, Ashish Raj, Javed Khan BhuttoAuthors Info & Claims
Ashwini Bari, Rajeev Yadav, Satish Kumar Alaria, Ashish Raj, Javed Khan BhuttoAuthors Info & ClaimsArticle No.: 138, Pages 1 - 9
Abstract
Sentiment analysis of product reviews plays a crucial role in understanding consumer preferences and guiding business strategies. This study aims to enhance sentiment analysis through improved machine learning algorithms. We introduce novel preprocessing methods and a hybrid model that combines different machine learning techniques to better interpret complex sentiments in product reviews. Our results demonstrate significant improvements in accuracy and processing efficiency compared to traditional models. These advancements offer valuable insights for businesses in tailoring products and services according to customer feedback. Future research may explore the integration of these techniques with real-time analysis systems for dynamic market adaptation. Moreover, the study explores the scalability of the proposed techniques in handling large datasets and diverse product categories. The enhanced algorithms showed robust performance across different types of reviews, indicating their applicability in a wide range of industries. The research also delves into the interpretability of machine learning models, ensuring that the sentiment analysis is not only accurate but also transparent and explainable. In conclusion, this study marks a significant step forward in the field of sentiment analysis. The improved machine learning techniques developed here provide a more reliable, efficient, and versatile tool for analyzing product reviews. These advancements have the potential to revolutionize how businesses interact with and respond to customer feedback, leading to more customer-centric products and services. Future work could focus on integrating these techniques with other forms of customer feedback analysis, such as social media monitoring, to provide a more comprehensive view of consumer sentiment.
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Published In
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
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Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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