Enhanced Customer Emotions Classifications in E-Commerce on Sentiment Analysis with Convolutional Neural Network

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Kamalakannan. R, Preethi. G

Abstract

In the rapidly evolving e-commerce landscape, accurately interpreting customer emotions from textual reviews is crucial for enhancing user experience and informing business strategies. Traditional sentiment analysis methods often struggle with the complexity and nuance of human emotions expressed in text. This research introduces an advanced approach employing Convolutional Neural Networks (CNNs) to improve the classification of customer emotions in e-commerce platforms. By leveraging CNNs' ability to capture local features and patterns within textual data, our model effectively distinguishes between subtle emotional cues, leading to more precise sentiment categorization. Experimental results demonstrate a significant improvement in classification accuracy over conventional techniques, underscoring the potential of deep learning models in sentiment analysis applications within the e-commerce sector.

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