Hybrid Models for Integrating Survey Data and AI-Driven Sentiment Analysis to Predict Consumer Trends
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-114Keywords:
Hybrid Models, Survey Data Integration, AI-Driven Sentiment Analysis, Consumer Trend Prediction, Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM), Multimodal Data Processing, Natural Language Processing (NLP)Abstract
This paper explores the development and application of hybrid models that integrate traditional survey data with AI-driven sentiment analysis to predict consumer trends. By combining structured data from surveys with unstructured data from online reviews and social media, these models offer a comprehensive understanding of consumer sentiments and preferences. The study examines various hybrid approaches, including the fusion of Convolutional Neural Networks (CNNs) with Bidirectional Long Short-Term Memory networks (Bi-LSTMs) for sentiment classification, and the integration of multiple AI models to process complex multimodal data. The effectiveness of these hybrid models is evaluated through case studies, demonstrating their superior performance in capturing nuanced consumer sentiments and enhancing the accuracy of consumer trend predictions
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