Accurate forecasting of Gross Domestic Product (GDP) is essential for informed policy-making and strategic economic planning. This paper proposes a hybrid deep learning model that integrates consumer sentiment data with traditional economic indicators to predict GDP growth. Utilizing a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, the model captures both semantic sentiment features and temporal economic patterns. Experimental results show that the hybrid model outperforms baseline machine learning algorithms and standalone deep learning approaches in terms of prediction accuracy. This research highlights the potential of sentiment-aware deep learning frameworks in enhancing macroeconomic forecasting and decision-making.

GDP Forecasting, Consumer Sentiment Analysis, Deep Learning, LSTM, CNN, Economic Prediction, Time-Series, NLP

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