Predicting Financial Market Trends using Time Series Analysis and
  Natural Language Processing
Ali Asgarov
Forecasting financial market trends through time series analysis and natural
language processing poses a complex and demanding undertaking, owing to the
numerous variables that can influence stock prices. These variables encompass a
spectrum of economic and political occurrences, as well as prevailing public
attitudes. Recent research has indicated that the expression of public
sentiments on social media platforms such as Twitter may have a noteworthy
impact on the determination of stock prices. The objective of this study was to
assess the viability of Twitter sentiments as a tool for predicting stock
prices of major corporations such as Tesla, Apple. Our study has revealed a
robust association between the emotions conveyed in tweets and fluctuations in
stock prices. Our findings indicate that positivity, negativity, and
subjectivity are the primary determinants of fluctuations in stock prices. The
data was analyzed utilizing the Long-Short Term Memory neural network (LSTM)
model, which is currently recognized as the leading methodology for predicting
stock prices by incorporating Twitter sentiments and historical stock prices
data. The models utilized in our study demonstrated a high degree of
reliability and yielded precise outcomes for the designated corporations. In
summary, this research emphasizes the significance of incorporating public
opinions into the prediction of stock prices. The application of Time Series
Analysis and Natural Language Processing methodologies can yield significant
scientific findings regarding financial market patterns, thereby facilitating
informed decision-making among investors. The results of our study indicate
that the utilization of Twitter sentiments can serve as a potent instrument for
forecasting stock prices, and ought to be factored in when formulating
investment strategies.