Real-Time Sentiment Analysis of Twitter Streams for Stock Forecasting |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-5 |
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| Year of Publication : 2024 | ||
| Authors : Prabhu Patel | ||
| DOI : 10.14445/22312803/IJCTT-V72I5P125 | ||
How to Cite?
Prabhu Patel, "Real-Time Sentiment Analysis of Twitter Streams for Stock Forecasting," International Journal of Computer Trends and Technology, vol. 72, no. 5, pp. 204-209, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I5P125
Abstract
In an effort to shed light on investor mood and market dynamics, this study explores the use of real-time sentiment analysis of Twitter streams for stock forecasting. The study investigates the potential, methodological developments, practical ramifications, and future directions of sentiment analysis in financial markets through a synthesis of findings from diverse research projects. The summary of results shows sentiment analysis’s enormous potential as a forecasting tool, providing insightful information about market patterns and investor mood. Advances in methodology, such as the use of machine learning algorithms and semantic techniques, have greatly improved the precision and efficiency of sentiment analysis models. These developments have opened the door for more resilient and flexible frameworks that can manage streams of data in real-time. Sentiment analysis is a practical tool that traders, investors, product developers, and business managers may use to acquire a competitive edge in the financial markets and inform decision-making processes. However, there are still issues that need to be investigated further, such as data preparation, model optimization, and semantic resource enhancement. The study emphasizes how real-time sentiment analysis in financial markets has the power to alter market dynamics and decision-making processes fundamentally. The predictive capacity of sentiment analysis is poised to transform the financial markets, providing new opportunities and insights for market participants as researchers continue to innovate and improve sentiment analysis approaches.
Keywords
Real-Time data streaming, Flink streaming, AI & ML, FinTech.
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