Application of natural language processing in financial news sentiment analysis for stock price prediction

Abstract

This thesis studies the application of Natural Language Processing (NLP) in the analysis of financial news sentiment and its subsequent impact on stock price prediction. With the increasing complexity of the financial market, the need for advanced computational techniques to predict stock price movement is evident. This study systematically reviews current research findings on the efficacy of NLP methods in analyzing the sentiment of financial news and stock price movements. By conducting a systematic literature review on the research from the past six years, this review emphasizes the development of sentiment analysis and NLP techniques and evaluates their predictive power.

A total number of 33 papers were chosen for this review. Key findings suggest that due to the recent advancement, particularly the introduction of the transformer model, the focus of NLP in stock prediction has shifted from traditional statistical-based feature representation to learning-based embedding methods.

The conclusion addresses the potential of sentiment analysis as a predictive tool and suggests directions for future research. This emphasizes the need for innovative NLP applications in the financial domain to enhance investment strategies and market understanding.