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Fine Tuned Cardiffnlp Twitter Roberta Base Sentiment Finance Dataset

Developed by arwisyah
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on a Twitter financial news sentiment dataset, specifically designed for financial text sentiment analysis.
Downloads 64
Release Time : 5/10/2024

Model Overview

A sentiment analysis model optimized for financial domain texts, capable of identifying sentiment tendencies in Twitter financial news.

Model Features

Financial Domain Optimization
Fine-tuned on financial news datasets for better understanding of financial terminology and context
High Accuracy
Achieves 88.82% accuracy on evaluation datasets
Easy Integration
Based on Hugging Face Transformers library, easily integrated into existing systems

Model Capabilities

Financial Text Sentiment Analysis
Twitter Text Processing
Three-class Sentiment Recognition

Use Cases

Financial Analysis
Market Sentiment Monitoring
Analyze market sentiment changes in financial news tweets
Help investors understand market sentiment trends
Risk Warning
Identify financial news with strong negative sentiment
Provide early warning signals for risk management
Media Analysis
News Impact Assessment
Evaluate sentiment tendencies after financial news releases
Understand the impact of news on market sentiment
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