Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Digital Market and Digital Currency
Manimegalai Subramanian
Department of Electrical and Electronics Engineering, Faculty of EEE, AMET University, Chennai, India
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Comparative Study of Deep Learning Architectures for Financial Sentiment Analysis in Digital Market Environments Lakshmi Dandapani; Manimegalai Subramanian
Journal of Digital Market and Digital Currency Vol. 3 No. 3 (2026): Regular Issue September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i3.83

Abstract

This study investigates the effectiveness of deep learning architectures for financial sentiment classification using textual data from financial reports and market statements. Four models, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN), were trained and evaluated on a dataset of 5,842 labeled financial sentences categorized as positive, negative, or neutral. The evaluation employed accuracy and weighted F1-score metrics to assess predictive performance and class balance. The results revealed that the CNN model achieved the highest performance with an accuracy of 70.32 percent and an F1-score of 70.22 percent, followed by the BiLSTM model with 67.49 percent accuracy and 66.86 percent F1-score. The CNN’s superior performance demonstrates its ability to capture local sentiment-bearing features efficiently, while BiLSTM excels in understanding bidirectional contextual dependencies. In contrast, the LSTM and GRU models performed less effectively due to their reliance on sequential memory mechanisms, which are less applicable to short and factual financial sentences. These findings highlight that CNN provides the best balance between accuracy, efficiency, and adaptability for financial sentiment classification and can serve as a foundation for intelligent digital market analysis systems and AI-driven financial decision support tools.
Co-Authors Lakshmi Dandapani