Sentiment-Enhanced Stock Price Prediction Using LSTM and Machine Learning Techniques

The stock market is highly complex and volatile, influenced by both positive and negative sentiments shaped by media releases. Accurate stock price analysis depends on the ability to recognize stock movements and identify underlying trends. Stock price prediction has long been an active area of research, but achieving ideal precision remains a challenging task. This paper proposes a combined approach that leverages efficient machine learning techniques alongside deep learning, specifically Long Short-Term Memory (LSTM) networks, to predict stock prices with greater accuracy. Sentiments derived from news headlines significantly impact traders' buying and selling decisions, as they tend to be influenced by the media. By integrating sentiment analysis with traditional technical analysis, we aim to enhance prediction accuracy. LSTM networks are particularly effective for learning and predicting temporal data with long-term dependencies. In our approach, the LSTM model utilizes historical stock data in conjunction with sentiment data from news items to build a more robust predictive model. This fusion of sentiment and technical analysis can improve the model's ability to predict stock price movements, offering a more comprehensive and accurate prediction mechanism for stock market behavior.