Application of Deep Learning for Product Review Sentiment Analysis in Predicting Market Demand Changes on E-Commerce Platforms
DOI:
https://doi.org/10.65310/v7bm9213Keywords:
Deep Learning, Sentiment Analysis, Product Reviews, Market Demand Prediction, E-Commerce.Abstract
The rapid development of e-commerce platforms has led to an increase in the volume of product reviews that reflect consumer perceptions and behavior, which can potentially be used as a basis for predicting changes in market demand. This study aims to examine the application of deep learning in product review sentiment analysis as a predictive instrument for market demand in e-commerce. The research method used is a literature study with a digital document analysis approach, through critical review of reputable journal articles, scientific proceedings, and research reports related to sentiment analysis, deep learning, and market demand prediction. The results and discussion show that deep learning-based sentiment analysis, particularly the transformer architecture, has a higher level of accuracy in understanding the context and emotions of review language compared to conventional methods, enabling it to represent consumer perceptions more comprehensively. Positive and negative sentiments are proven to correlate with increases and decreases in market demand, making them suitable for use as predictive variables. In conclusion, the integration of deep learning-based sentiment analysis in market demand prediction models can improve the accuracy of business decision-making and support e-commerce strategies that are adaptive to the dynamics of the digital market..
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Copyright (c) 2025 Harwati Harwati, Laili Afiatur Rosidah, Rika Rahmawati Milya (Author)
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