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Roberta Large Financial News Sentiment En

Developed by Jean-Baptiste
This model is fine-tuned for sentiment classification of financial news (especially Canadian news), trained on a mixed dataset, and particularly suitable for Canadian financial news analysis.
Downloads 969
Release Time : 12/28/2022

Model Overview

A financial news sentiment classification model fine-tuned based on the RoBERTa-large architecture, specifically designed to analyze sentiment tendencies (negative/neutral/positive) in financial news texts, with excellent performance on Canadian financial news.

Model Features

Specialized Optimization for Canadian Financial News
Additional training on 2000 annotated Canadian financial news articles, achieving an F1 score of 83.6% in this domain
High-Quality Annotated Data
Only sentences with at least 75% annotator agreement were retained to ensure label reliability
Three-Class Fine-Grained Classification
Distinguishes between negative/neutral/positive sentiment states, rather than simple binary classification

Model Capabilities

Financial text sentiment analysis
News sentiment classification
Specialized analysis for Canadian market news

Use Cases

Financial Market Analysis
Sentiment Monitoring of Corporate Earnings Reports
Analyzing sentiment tendencies in corporate earnings news
Can identify 'revenue growth of 17%' as positive and 'net income decline of 3%' as negative
Market Risk Warning
Detecting negative news events such as bankruptcy announcements
Accurately identifies 'filing for bankruptcy protection' as negative sentiment (confidence >93%)
Investment Decision Support
Analysis of Mining Company Production Reports
Assessing sentiment tendencies in mining company production announcements
Correctly classifies 'solid production performance' as positive sentiment
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