Published in: Journal of Computer Science and Engineering in Innovations and Research (ISSN No: 3049-1762 online)
Publication Date: June 15, 2025
sentiment analysis is a methods for analysis the tweets on different dataset. This approach involves extracting insights from various sources like financial reports,news to assess public sentiment regarding market trends. By leveraging sentiment analysis techniques, businesses and traders can predict stock market fluctuations, identify risks, and enhance decision-making processes.There are different techniques for sentiment analysis on financial data.These techniques help classify textual data and uncover hidden patterns in financial sentiments.This study discuss about sentiment analysis on financial dataset.
Financial Sentiment Analysis, Stock Market Prediction, Machine Learning, Deep Learning, Algorithmic Trading, Market Trends
This study discuss on aspect of modern finance that involves extracting, interpreting, and evaluating sentiments on financial tweets [1]. The objective is to gain insights into market behavior and make data-driven investment decisions [2]. The increasing reliance on automated trading and financial forecasting has made sentiment analysis an essential tool for investors and financial institutions [2]. The researchers identified market trends and investor sentiment—critical factors influencing financial markets [1], [3]. Previous studies have explored different methodologies, to improve the accuracy of financial sentiment classification [2], [4]. Ambiguous terms, sarcasm, and context-dependent meanings further complicate automated analysis systems [6], [7]. In this study real-time sentiment evaluation occurred for enable timely decision-making [5].
Sentiment analysis in finance involves processing large amounts of textual data [8]. This section discuss different techniques for analysis the sentiment and their impact on market predictions .
Despite advancements in sentiment analysis, challenges such as linguistic ambiguity and data sparsity continue to impact accuracy [6]. This section highlights existing research gaps and outlines the objectives of the study, including improving sentiment classification accuracy and enhancing real-time analysis capabilities [4].