Discovering Untapped Potential in Finance Markets Using NLP-Driven Sentiment Analysis

Abstract

Objectives: The main objective in Sentiment Analysis, and financial data is to decode sentiments through the use of Natural Language Processing (NLP) and Machine Learning (ML). This provides insights into market trends that are essential for risk management and well-informed financial decisions. By removing emotion from financial writing, gives stakeholders practical advice for wise investments in dynamic markets. The main objective is to increase the accuracy of Multinomial Naïve Bayes (Multinomial NB). In this work, we used purely ML models to get the accuracy more than the previous works, which got 74%, and in this work, we can see the accuracy increased to approximately 82%. Methods: This specific Research Work employs Machine Learning approaches to extract sentiment polarity (positive, negative, and neutral) from financial text data. The used ML models are- Multinomial Naïve Bayes (Multinomial NB), Logistic Regression (LR), KNeighbors, Decision Tree, and Random Forest algorithms are used for Sentiment Analysis. Findings: Studies conducted in the financial sector have shown that sentiment and informational substance in stock news have a big impact on market factors such as trading volume, volatility, stock prices, and corporate earnings. This study involves approximately 6000 newspaper articles to extract the polarity from financial text data. The incorporation of Multinomial NB of Natural Language Processing techniques results in a marginal improvement in the sentiment classification model performance among all the models used, where the previous works got the lowest accuracy of 74%. Novelty: This research introduces advanced ML models for extracting financial sentiment from text, including Random Forest, LR, KNeighbors, Decision Tree, and Multinomial NB previously unexplored in similar studies. Notably, the Multinomial NB model accuracy rose significantly from 65% to 82%. The study also presents sentiment segregation (positive, negative, neutral) visualized in a donut chart. Keywords: Sentiment Analysis, Machine Learning, Natural Language Processing, Financial data, Stock market prediction