Determining the Accuracy of Reinforced Model via Sentiment and Technical Analysis as a Stock Market Prediction Technique

The application of sentiment analysis as a stock market prediction technique has garnered significant attention in recent years, being particularly useful when catastrophic situations affect the economy. This research paper aims to determine the accuracy of sentiment analysis in predicting stock market movements by developing a reinforced model that integrates both sentiment and technical analysis. While previous studies have focused on social media sentiment for stock price prediction, this research proposes a reinforced model that combines sentiment analysis with technical analysis indicators to enhance accuracy. The study develops and tests predictive models for stock price and trend forecasting using a large-scale sample of tweets from four prominent companies: Apple, Google, Microsoft, and Netflix. The results of the study provide valuable insights into the accuracy of the reinforced model combining sentiment and technical analysis for stock market prediction, offering a more comprehensive approach to understanding market sentiment and its impact on stock prices. Further, the findings contribute to the existing knowledge on stock market prediction techniques, and underscore the value of considering multiple factors in decision-making processes.