Authors : Abhinav Sudhakar Dubey; Pranav Singh Mahara

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/3hck5bnh

Scribd : https://tinyurl.com/743bj8nw

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN1691

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This paper presents a novel multimodal deep learning framework for analyzing news sentiments and forecasting market movements by leveraging natural language processing, deep learning, and auxiliary data sources. Traditional methods often rely solely on textual news data, limiting their predictive power due to the complexity and ambiguity of language. Our approach incorporates additional modalities such as stock prices, social media sentiment, and economic indicators to capture a more comprehensive view of market dynamics. We employ a hybrid deep learning architecture that combines convolutional neural networks (CNNs) for text feature extraction, long short-term memory (LSTM) networks for capturing sequential dependencies, and attention mechanisms to selectively focus on the most relevant features. To address data scarcity, we introduce advanced data augmentation techniques, generating synthetic news headlines based on historical stock price movements and sentiment patterns. The proposed system is evaluated on a comprehensive dataset spanning multiple years, including news headlines, stock prices, social media data, and economic indicators. Our method achieves an accuracy of 77.51%, significantly outperforming traditional methods and demonstrating improved robustness and predictive power. This study highlights the potential of integrating diverse data sources and sophisticated deep learning techniques to enhance news sentiment analysis and market movement forecasting.

Keywords : Multimodal Deep Learning, Natural Language Processing, Sentiment Analysis, Convolutional Neural Networks, Long Short-Term Memory, Attention Mechanisms, Data Augmentation, Auxiliary Data.

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