Hybrid LSTM-CNN deep learning framework for stock price prediction with google stock and reddit sentiment data

Abstract

   This study evaluates a hybrid model that integrates Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) to predict stock prices. The model leverages two datasets: historical Google stock data and sentiment data from Reddit comments. Sentiment analysis was performed using VADER from NLTK, which classified comments as negative, neutral, or positive, while a CNN model was trained to predict sentiment scores. Separately, an LSTM model was built using ten years of Google stock data from Yahoo Finance, with features scaled using MinMax normalization to improve learning and a dropout layer added to prevent overfitting. Model performance was evaluated using Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). The LSTM model performed well on test data but showed lower accuracy on unseen data during forecasting. The hybrid model successfully combined the outputs of both the CNN and LSTM, demonstrating superior performance with lower RMSE and higher classification accuracy compared to the standalone models. This highlights the potential of integrating sentiment analysis with traditional stock prediction. The study acknowledges challenges in classifying neutral sentiments, suggesting that more comprehensive sentiment data is needed for future research.