Financial Sentiment Analysis Using Artificial Intelligence: A Review of Techniques and Applications
Abstract
Financial sentiment analysis has emerged as an important research domain due to the growing influence of social media, financial news, and investor opinions on market behavior. This review paper explores the use of Artificial Intelligence techniques for extracting and analyzing financial sentiments from textual and multimedia data sources. The study reviews machine learning, deep learning, transformer models, and natural language processing approaches applied to stock market prediction, investor behavior analysis, and economic forecasting. Existing literature is systematically analyzed to identify commonly used datasets, feature extraction methods, sentiment classification frameworks, and performance evaluation metrics. The paper also examines challenges related to multilingual financial texts, misinformation, sarcasm detection, and real-time sentiment processing. The review concludes that AI-driven sentiment analysis has become a valuable tool for intelligent financial forecasting and strategic investment decision-making.
References
Kaidhapuram, S. R. (2023). Composable architecture for enterprises: Principles, adoption patterns, and strategic impact. International Journal of Computer Techniques, 10(4). https://ijctjournal.org/composable-architecture-enterprises/
Bellundagi, M. (2022). Performance Optimization Techniques for Enterprise Java Applications Using Middleware and Messaging Systems. International Journal of Computer Technology and Electronics Communication, 5(3), 5158-5168.
Kaidhapuram, S. R. (2020). Microservices Architecture and Real-Time Streaming for Pharmaceutical Use-Cases: A Technical Examination of Distributed Systems in Pharmaceutical Discovery, Production, and Regulatory Adherence. International Journal of Computer Science Engineering Techniques, 4(3), 1–8. https://www.ijcsejournal.org/
Bellundagi, M. (2023). Integrating Machine Learning with Business Rule Management Systems for Adaptive Enterprise. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8023-8039.
Bellundagi, M. (2023). Design of an Intelligent Clinical Decision Support System Using Machine Learning Techniques. International Journal of Research and Applied Innovations, 6(6), 10075-10081.
Konda, P. R. (2018). Integrating LLMs into Financial Data Analysis Workflows for Automated Interpretation and Insights . International Numeric Journal of Machine Learning and Robots, 2(2). https://injmr.com/index.php/fewfewf/article/view/231
Chawla, N., & Dasnam, S. V. (2023). Optimize Resource Allocation and Sprint Forecasting in Financial Agile Projects. Sch J Eng Tech, 12, 327-333.
Badri, P., Nerella, A., & Chawla, N. (2023). AI/ML-Based Retail Banking Transactions Forecast Application using Complex Neural Networks Optimization Algorithm. Available at SSRN 5282871.
Konda, P. R. (2024). Human-Centric AI: Bridging Emotional Intelligence with Computational Efficiency. (2024). International Machine Learning Journal and Computer Engineering, 7(7). https://mljce.in/index.php/Imljce/article/view/65
Konda, P. R. (2024). Adaptive Data Analytics Using Ethical AI Agents and Logic-Based Compliance Engines . International Numeric Journal of Machine Learning and Robots, 8(8). https://injmr.com/index.php/fewfewf/article/view/233
Bellundagi, M. (2024). A Multi-Layer AI-Driven Decision Intelligence Framework for Enterprise and Healthcare System. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11679-11687.
Bellundagi, M. (2024). A Scalable Microservices Architecture for Enterprise Payment Systems Using Java and Cloud Platforms. International Journal of Computer Technology and Electronics Communication, 7(2), 8543-8553.
Bellundagi, M. (2024). An Intelligent Digital Transformation Framework for Smart Enterprises Using AI and Cloud Computing. International Journal of Science, Research and Technology, 7(4), 12433-12446.
Konda, P. (2021). End-to-End Governance Strategies for Secure Multi-Domain Cloud Analytics. International Journal of Management Education for Sustainable Development, 4(4). Retrieved from https://ijsdcs.com/index.php/IJMESD/article/view/705/268
Kaidhapuram, S. R. (2024). Zero ETL Integration and Data Fabric for Analytics Warehouses: Eliminating Pipeline Friction in the Modern Analytical Stack. International Journal of Computer Science Engineering Techniques, 8(5), 1–12. https://www.ijcsejournal.org/
Konda, P. R. (2024). Semantic Emergence Modeling: How AI Systems Develop Higher-Level Understanding from Raw Data. International Meridian Journal, 6(6). https://meridianjournal.in/index.php/IMJ/article/view/118
Konda, P. R. (2024). Digital Transformation in Banking: Navigating the Technological Frontier. . International Machine Learning Journal and Computer Engineering, 7(7), 1-13. https://mljce.in/index.php/Imljce/article/view/21
