## 1 Introduction  
Stock market investment, a cornerstone of global business, has experienced unprecedented growth, becoming a lucrative, yet complex field [1, 2]. Predictive models, powered by cutting-edge technologies like artificial intelligence (AI), sentiment analysis, and machine learning algorithms, have emerged to guide investors in their decision-making processes [3, 4, 5]. Key among these techniques are convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM), all rooted in neural network methodologies. These intelligent software systems assist traders and investors in augmenting their trading strategies [6]. However, existing predictive models struggle to qualify with unforeseen market events, influenced by intricate external factors such as economic trends, market dynamics, firm growth, consumer prices, and industry-specific shifts. These factors impact stock prices, leading to unpredictable outcomes [7, 8]. Hence, a fundamental analysis integrating economic factors and the ability to analyze financial news and events is imperative. Historical datasets, fundamental to stock models, often contain noisy data, demanding meticulous handling for accurate predictions. The volatile nature of stock markets, characterized by rapid fluctuations, requires precise predictions [9, 10]. Diverse sources of stock market data, including media, news headlines, articles, and tweets, play a crucial role. Natural language processing (NLP) algorithms, particularly sentiment analysis, enable the extraction of sentiments from social media, news feeds, or emails. Sentiments are categorized as positive, negative, or neutral through machine learning (ML) or deep learning (DL) algorithms. This research pioneers a unique approach by combining deep reinforcement learning (DRL) and sentiment analysis from the NLP paradigm, resulting in a robust cognitive decision-making system. DRL processes multidimensional and high-dimensional resource information to generate output actions based on input data without supervision, addressing complexities posed by rapid market changes, incomplete information, and various external factors. This paper presents significant contributions, including:  
* The utilization of NLP to preprocess news and media data and discern market sentiments related to stocks. Fine-tuning BERT is employed in conjunction with TF-IDF to achieve maximum accuracy.
* Sourcing historical stock price datasets from reputable platforms such as S&P, Yahoo, NASDAQ, etc.
* Application of variation mode decomposition (VMD) for signal decomposition, followed by LSTM implementation to predict prices.
* Implementation of DRL, integrating NLP, historical data, and sentiment analysis from media sources to predict stock market prices for specific businesses based on agents and actions.  
The subsequent sections of this paper are organized as follows. Section 1 provides a comprehensive literature review on the topic, while Section 3 discusses the necessary background. Section 4 outlines the problem statement and algorithms employed in the project. Section 5 presents the proposed architecture for stock prediction, delving into its components. The implementation and results are discussed in Section 6, and finally, Section 7 concludes the paper.