Sentiment Analysis of Financial News Data Using Machine Learning Techniques

Ratnesh Pathak , Mohit Singh , Dr. Santosh Singh , Amit Pandey

Sentiment Analysis, Financial News, Machine Learning, Support Vector Machine, Natural Language Processing, Model Evaluation, Data Preprocessing, Financial Forecast.

Sentiment analysis of financial news data using machine learning techniques involves the intelligent classification of news content to gauge the emotional tone—positive, negative, or neutral—towards financial markets, companies, and economic events. This process helps uncover deeper, real-world insights into how information drives investor behaviors and market dynamics, making it a vital tool for analysts, traders, and financial institutions. Sentiment analysis breathes fresh life into the flood of financial news. Imagine an investor sifting through headlines and press releases in real time—not just to absorb facts, but to perceive the prevailing mood swirling around the markets. By leveraging machine learning, this task shifts from a tedious manual process to a dynamic, automated understanding of market tone. Researchers train algorithms using thousands of headlines and articles, cleansing the text and transforming headlines into digestible signals using techniques like Bag of Words, TF-IDF, and word embeddings (Word2Vec, Fin BERT). Once textual features are extracted, classifiers (such as Multinomial Naïve Bayes, Logistic Regression, K-Nearest Neighbors, and deep learning models like LSTM and BERT) are unleashed to spot sentiment nuances hiding in the language. Transformer-based models—like BERT—capture subtle context, irony, and complex market jargon, raising accuracy to new heights. The best systems now routinely achieve classification accuracy above 80%, even on noisy, real-world news feeds. What makes this truly human is the integration of market knowledge: models aren’t just reading text, but linking sentiment scores with actual stock movements and trading patterns. Negative news, for instance, can trigger immediate market reactions; positive sentiment often shows subtler effects. By marrying textual analysis with market trends, sentiment analysis allows investors to anticipate volatility, spot opportunities, and avoid risks, even as news flows in from every corner of the globe.

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"Sentiment Analysis of Financial News Data Using Machine Learning Techniques ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 9, page no.b219-b225, September-2025, Available :https://ijsdr.org/papers/IJSDR2509129.pdf

Volume 10 Issue 9, September-2025

Pages : b219-b225

Paper Reg. ID: IJSDR_305001

Published Paper Id: IJSDR2509129

Downloads: 000162

Research Area: Commerce All

Country: Mumbai, Maharashtra, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2509129

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2509129

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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