arXiv (All)
open access
Financial sentiment analysis using FinBERT with application in predicting stock movement
In this study, we integrate sentiment analysis within a financial framework by leveraging FinBERT, a fine-tuned BERT model specialized for financial text, to construct an advanced deep learning model based on Long Short-Term Memory (LSTM) networks. Our objective is to forecast financial market trend
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SARCASAM Analysis in Social Networks using Deep Learning#719838
- The Credit Realization Law: Exact Memory Limits and Minimal Trace Architectures for Online Recurrent Learning#478437
- Convolutional Neural Networks for Sentence Classification#6459
- Econstellar: An Open-Source AI-Augmented Research Engine for Computational Financial Econometrics#259543
- A SPOT in the dark: using AI to assess financial stability risks#426946
Record · ID 1051948
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive