Financial News Sentiment Analysis Using NLP and Machine Learning for Asset Price Prediction: A Systematic Review
DOI:
https://doi.org/10.21015/vtse.v13i3.2165Abstract
Forecasting market movements in stocks, gold, and crude oil requires a deep understanding of how financial news sentiment influences asset prices. Analyzing news sentiment is crucial for understanding market dynamics and forecasting price fluctuations. However, creating accurate financial news datasets, particularly in terms of proper labeling and sourcing, continues to be a significant challenge. This paper presents a comprehensive literature review on financial news sentiment analysis and its application in market trend prediction.By reviewing articles in reputable journals from 2018–2025, we consolidate key findings, including techniques for dataset creation, labeling, and sourcing, as well as the use of advanced methods such as Natural Language Processing (NLP) and deep learning models. This review contributes to the growing literature on sentiment analysis in the context of the relationship between stocks and commodities, especially gold, crude oil, and the role of global and market specific news sentiments in determining the assets prices. The study focuses on issues that concern researchers in this regard; it also compares the relative success of various prediction models and discusses the criteria for assessing their effectiveness.We propose solutions to current challenges and outline future research directions to improve sentiment analysis in financial markets.
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