Predictive Precision: LSTM-Based Analytics for Real-time Stock Market Visualization

Artificial intelligence (AI) is widely implemented in finance for stock price prediction using deep learning techniques. However, stock data’s high volatility and signal-to-noise ratio can make it difficult for neural networks (NN) to make accurate predictions. Nevertheless, even 70% accuracy can provide a good return value, leading to the increasing adoption of AI. In this study, we utilized long short-term memory (LSTM), a time series forecasting method, to predict the future stock market prices of Twitter (TWTR) and Facebook (FB) on NYSE and evaluated the performance of the model. Additionally, sentiment analysis, which involves understanding information from sentiment rich sources such as news and social media, plays a crucial role in predicting changes in stock market prices. With the abundance of data in modern times, manual data examination is no longer practical, necessitating AI and data mining techniques. The impact of emotion classification on stock market prices was also investigated in this study. The deep learning method involved using an LSTM neural network to learn the features instead of relying on human feature engineering. The NLP model used in this study combines LSTM, an intriguing branch of machine learning that uses past data to predict future values, with real-time visualization for sentiment analysis.