Analytics and Research Project: Analyzing Retail Sentiment with Current Methodology and Emerging Technology
Date
2024-08
Authors
Patel, Mansiben Ravindrakumar
Major Professor
Townsend, Anthony M
Advisor
Committee Member
Anthony M
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Abstract
Understanding consumer sentiments and preferences is crucial for retail companies to enhance product offerings and optimize marketing strategies. This paper explores the application of sentiment analysis in understanding consumer perceptions from online retail reviews. Leveraging a dataset from an online retail platform, sentiment analysis techniques are employed to categorize reviews into positive and neutral sentiments. Visualizations including pie charts, word clouds, and bar charts illustrate sentiment distributions and identify prominent topics consumers discuss.
Additionally, the research investigates how Generative AI is revolutionizing sentiment analysis in the retail sector. Generative AI is discussed for its ability to automate sentiment analysis processes, handle large-scale unstructured data, and uncover nuanced consumer insights that traditional methods may overlook. The purpose of integrating generative artificial intelligence (AI) is to improve sentiment analysis's precision, effectiveness, and scalability. This will enable retail firms to make data-driven decisions that are in line with their customers' preferences.
This paper contributes to advancing the understanding of sentiment analysis methodologies in retail contexts through empirical analysis and literature review. It underscores the potential of Generative AI to revolutionize sentiment analysis practices, paving the way for future research and practical applications in consumer-centric retail strategies.
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Type
creative component
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Rights Statement
Attribution-NoDerivs 3.0 United States
Copyright
2024