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Development and Application of Machine Learning Algorithms for Sentiment Analysis in Digital Manufacturing: A Pathway for Enhanced Customer Feedback
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Author(s): Vishal Jain (Sharda University, India)and Archan Mitra (Presidency University, India)
Copyright: 2024
Pages: 13
Source title:
Emerging Technologies in Digital Manufacturing and Smart Factories
Source Author(s)/Editor(s): Ahdi Hassan (Global Institute for Research Education and Scholarship, The Netherlands), Pushan Kumar Dutta (Amity University, Kolkata, India), Subir Gupta (Swami Vivekanand University, India), Ebrahim Mattar (College of Engineering, University of Bahrain, Bahrain)and Satya Singh (Sharda University, Uzbekistan)
DOI: 10.4018/979-8-3693-0920-9.ch002
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Abstract
Customer input has increased as digital manufacturing and smart factories advance. However, standard analysis methods struggle to turn this feedback into useful insights. This research study examined the use of machine learning (ML) sentiment analysis algorithms to improve digital manufacturing customer feedback interpretation. Machine learning, sentiment analysis, and digital industrialization theories underpin the research. Sentiment analysis may reveal nuanced consumer feedback insights that traditional methods miss, according to customer experience management and complex data analytics theories. A specially constructed ML system for sentiment analysis was used to real-world customer feedback data from numerous digital manufacturing enterprises in a case study. This method classified feedback sentiment using natural language processing. The program picked up small changes in client emotions that previous methods missed. These findings imply that machine learning-based sentiment analysis improves digital manufacturing customer feedback interpretation.
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