## 2 Related Work  
Stock price prediction efforts have centered on supervised learning techniques, such as neural networks, random forests, and regression methods [11]. A detailed analysis by authors [12] underscored the dependency of supervised models on historical data, revealing constraints that often lead to inaccurate predictions. In a separate study [13], speech and deep learning (DL) techniques were applied to stock prediction using Google stock datasets from NASDAQ. The research demonstrated that employing 2D principal component analysis (PCA) with deep neural networks (DNN) outperformed the results obtained with two-directional PCA combined with radial bias function neural network (RBFNN), highlighting the efficacy of specific methodologies in enhancing accuracy. Another comprehensive survey [14] explored various DL methods, including CNN, LSTM, DNN, RNN, RL, and others, in conjunction with natural language processing (NLP) and WaveNet. Utilizing datasets sourced from foreign exchange stocks in Forex markets, the study employed metrics like mean absolute percentage error (MAPE), root mean square error (RMSE), mean square error (MSE), and the Sharpe ratio to evaluate performance. The findings highlighted the prominence of RL and DNN in stock prediction research, indicating the increasing popularity of these methods in financial modeling. While this study covered a wide array of prediction techniques, it notably emphasized the absence of results related to combining multiple DL methods for stock prediction. In a different studies [15, 16], four DL models utilizing data from NYSE and NSE markets were examined: MLP, RNN, CNN, and LSTM. These models, when trained separately, identified trend patterns in stock markets, providing insights into shared dynamics between the two stock markets. Notably, the CNN-based model exhibited superior results in predicting stock prices for specific businesses. However, this study did not explore hybrid networks, leaving unexplored potential in creating combined models for stock prediction. Additionally,advances in machine learning have led to considerable progress in speech recognition, language processing, and image classification across various applications [17]. Researchers have applied digital signal processing methods to stock data, particularly focusing on time series data analysis [18]. Moreover, reinforcement learning (RL) has emerged as a method capable of overcoming the limitations of traditional supervised learning approaches. By combining financial asset price prediction with the allocation step, RL algorithms can make optimal decisions in the complex stock market environment [19]. While LSTM techniques have been extensively researched for stock prediction due to their ability to efficiently process large datasets, challenges arise from the need for substantial historical data and considerable computational resources [20]. A critical issue with LSTM models is their limited capacity to offer rational decisions to investors, such as whether to buy, sell, or retain stocks based on predictions [21]. However, a recent study [22] demonstrated the potential of combining LSTM with sentiment analysis, providing valuable support to stock investors in decision-making processes. Furthermore, researchers have explored support vector machine (SVM) techniques in time series prediction. Despite their accuracy, SVM models require extensive datasets and involve time-consuming training processes [23]. In the comprehensive review of existing literature, it became evident that both supervised and unsupervised machine learning models have limitations, despite their efficiency in predicting time series data. Researchers have identified specific challenges associated with raw data characteristics, leading to barriers to accurate stock market predictions [24,25]. To address these limitations, this paper introduces a novel approach that integrates deep reinforcement learning (DRL) and sentiment analysis.