PREDICTING STOCK PRICE MOVEMENTS ON THE QATAR STOCK EXCHANGE USING TWITTER SENTIMENT AND MACHINE LEARNING MODELS

Abstract

ABSTRACT The advancement of artificial intelligence (AI) along with machine learning (ML) systems is transforming the financial markets in distinctive and novel ways. This thesis demonstrates conclusively that Twitter sentiment analysis can be used to predict stock price movements in the Qatar Stock Exchange (QSE). The detailed analysis of the QSE companies' tweets necessitates the application of NLP (natural language processing) techniques that sentiment score and correlate those scores with stock market data. Moreover, a machine learning sentiment analysis model is constructed to predict short-term price movements with social media sentiment data. This analysis contributes significantly to the literature of financial sentiment analysis, emphasizing how social discourse affects market movement. Undoubtedly, the results from this research will assist investors, decision-makers, and financial market experts in emerging economies.

Date of Award	2025
Original language	American English
Awarding Institution	HBKU College of Science and Engineering