Peer-Reviewed Research
Investigating Stock Prediction Using LSTM Networks and Sentiment Analysis of Tweets Under High Uncertainty: A Case Study of North American and European Banks
Research focused on stock prediction using LSTM networks and sentiment analysis, combining deep learning, natural language processing and financial forecasting.
- Artificial Intelligence
- LSTM
- Deep Learning
- Natural Language Processing
- Sentiment Analysis
- Time Series
- Financial Forecasting
Research Impact
Academic reach beyond the original publication.
Referenced across peer-reviewed journals, IEEE conferences and international research publications in AI, forecasting, sentiment analysis and deep learning.
Citation count displayed by ResearchGate for the publication.
Citing-paper count displayed by IEEE Xplore for the publication.
Full-text views recorded on IEEE Xplore.
Selected citing works currently span the period from 2024 through 2026.
Snapshot · August 2026 · Source-specific metrics shown separately · Citation and view counts may change over time.
Publisher Reach
Referenced across major research ecosystems.
IEEE Journals & Conferences 
Springer Journals, Books & Proceedings 
Elsevier Research Journals 
Emerald Publishing Academic Publishing 
Sciendo Peer-Reviewed Journals 
SSRN Research Network Institutional Reach
Selected affiliations represented among authors of citing works.
Journals & Venues
Citation reach across journals, conferences and research series.
Selected Citations
Research building on related forecasting, AI and sentiment-analysis themes.
Comparative Evaluation of FinBERT and IndoBERT for LSTM-Based Stock Price Prediction in Indonesia
Heri Santosa · Kusrini Kusrini · Rodrigo Martínez-Béjar
Jurnal Masyarakat Informatika · Volume 17(2) View PublicationEnhancing Stock Market Price Prediction Through FinGPT-Driven Sentiment Analysis, Technical Indicators, Historical Price Data, and Attention-Based Hybrid Deep Learning Models
Mobin Golabzaei · Esmaeil Taheripour · Seyed Jafar Sadjadi
Scientific Reports View PublicationAdvanced Deep Statistical Learning Approach for Forecasting Global Economic Policy Uncertainty and Volatility
Khder Alakkari · Mostafa Abotaleb · El-Sayed M. El-kenawy · Pradeep Mishra
Computational Economics View PublicationDeep Transformer Q-Learning Based Reinforcement Learning for Portfolio Optimization of Cryptocurrencies
Matias Zepeda · Werner Kristjanpoller · Marcel C. Minutolo
Applied Soft Computing · Volume 195 View PublicationA Bidirectional LSTM–Sentiment Fusion Framework for Dynamic Financial Market Prediction
Minal Dhankar · Neha Gupta
International Journal of Advanced Computer Science and Applications · Volume 17(4) View PublicationStock Price Prediction Accuracy Utilizing Social Media Sentiment
Meldas Jaskelevičius
Vilnius University Open Series View PublicationMarketSenseAI 2.0: Enhancing Stock Analysis Through LLM Agents
George Fatouros · Kostas Metaxas · John K. Soldatos · Manos Karathanassis
IEEE International Conference on Data Mining Workshops (ICDMW) View PublicationA Novel Hybrid GCN-LSTM Algorithm for Energy Stock Price Prediction: Leveraging Temporal Dynamics and Inter-Stock Relationships
Babak Amiri · Amirali Haddadi · Kosar Farajpour Mojdehi
IEEE Access · Volume 13 View PublicationArtificial Intelligence Investment, Executive Digital Background, and Stock Price Synchronization
Kai Zheng · Yongping Ruan · Yanan He
Finance Research Letters · Volume 86 View PublicationRevisiting Time-Varying Dynamics in Stock Market Forecasting: A Multi-Source Sentiment Analysis Approach with Large Language Model
Zhiqi Shao · Xusheng Yao · Feng Chen · Ze Wang · Junbin Gao
Decision Support Systems · Volume 190 View PublicationBooks & Proceedings
Selected references appearing in edited academic volumes.
Innovations and Advances in Cognitive Systems
Includes the chapter “Stock Market Forecasting Using a Novel Conv-LSTM Deep Learning Model”
View ChapterInnovative Computing and Communications
Includes the chapter “A Review of Techniques Used for Social Networks’ Sentiment Analysis”
View Chapter
Ordine degli Ingegneri della Provincia di Roma 
Professional Engineering Journal