 AI-Based Sentiment Analysis for Stock Market Prediction: A Systematic Literature ReviewFanyi Zhao and Tianxing Tang Journal of Sustainability, Policy, and Practice, 2026, vol. 2, issue 3, 115-124 Abstract: The integration of artificial intelligence and natural language processing into financial market prediction has attracted significant research attention over the past decade. This review systematically examines the landscape of AI-driven sentiment analysis techniques applied to stock market movement prediction, covering 87 studies published between 2011 and 2024. A taxonomic framework is proposed to categorize existing approaches along four dimensions: data sources, sentiment extraction techniques, correlation modeling strategies, and evaluation metrics. Quantitative comparisons across lexicon-based, machine learning, deep learning, and large language model paradigms reveal that transformer-based models achieve the highest directional accuracy (up to 65.28%) on standard benchmarks, while lexicon-based methods retain advantages in computational efficiency and interpretability. Key challenges including data noise, temporal decay, and cross-market generalizability are critically assessed, alongside emerging trends in multimodal fusion and explainable AI for financial sentiment. Keywords: sentiment analysis; stock market prediction; natural language processing; deep learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:dba:jsppaa:v:2:y:2026:i:3:p:115-124 Access Statistics for this article More articles in Journal of Sustainability, Policy, and Practice from Pinnacle Academic Press |
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