Analyzing financial market trends using time series analysis and natural language processing is a complex task due to the multitude of variables that can influence stock prices. These variables include economic and political events, as well as public attitudes. Recent research suggests that sentiments expressed on social media platforms like Twitter can significantly impact stock prices. This study aimed to evaluate the viability of Twitter sentiments as a tool for predicting the stock prices of major corporations like Tesla and Apple. The study found a strong association between emotions conveyed in tweets and fluctuations in stock prices. Positivity, negativity, and subjectivity were identified as the primary factors influencing these fluctuations. The data was analyzed using the Long-Short Term Memory neural network (LSTM) model, which is recognized as the leading methodology for predicting stock prices using Twitter sentiments and historical stock price data. The models used in the study demonstrated high reliability and provided accurate outcomes for the designated corporations. In conclusion, this research highlights the importance of considering public opinions when predicting stock prices. The application of Time Series Analysis and Natural Language Processing can yield valuable insights into financial market patterns, enabling informed decision-making for investors. The study’s findings suggest that incorporating Twitter sentiments can be a powerful tool for forecasting stock prices and should be considered when developing investment strategies.
Using Time Series Analysis and Natural Language Processing to Forecast Financial Market Trends. (arXiv:2309.00136v1 [q-fin.ST])
