Emotion Analysis of Online Consumer Reviews Using Intrusive Weed Management
Abstract
Feedback from customers on any service or product is critical in the advertising industry for promoting new features. Questionnaire and rating data are being analyzed by researchers in order to better understand customer behavior. Text evaluation is more successful when people share their feelings verbally. This article mines consumer sentiment by analyzing the text review content of a product or service. The stopword approach was used to preprocess the input material, and the term frequency feature was used to modify the numerical data. The work employed the Invasive Wildflower Optimization Algorithm to enhance the content quality into two classes: positive and negative reviews. The fitness function's efficiency is increased via numerical conversion.
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