# Financial Sentiment Analysis

A financial domain sentiment analysis model fine-tuned based on econo-sentence-v2, achieving 96.2% accuracy on the Financial Phrase Bank dataset

This model is a financial sentiment classification model fine-tuned on FinBERT, capable of categorizing financial texts into negative, neutral, and positive classes.

Modern-FinBERT-large is a pre-trained NLP model specifically designed for financial sentiment analysis. Based on the ModernBERT-large language model, it is trained on large-scale financial corpora to adapt to financial text classification tasks.

Financial text sentiment analysis model fine-tuned based on Llama-3-8B, supporting positive/neutral/negative three-class classification

FinBERT fine-tuned financial sentiment analysis model, optimized for financial text sentiment classification

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.

FinMA-7B-NLP is a large language model developed by the Pixiu Project for the financial domain, specifically designed to understand complex financial terms and concepts. It has undergone natural language instruction fine-tuning and excels in financial NLP tasks.

SSAF-FinBert is a sentiment analysis model for stock market news, fine-tuned from FinBert, capable of classifying financial texts into positive, negative, and neutral sentiments.

Financial news sentiment analysis model fine-tuned on DeBERTa-v3-small, achieving an F1 score of 0.9940 on the evaluation set

A fine-tuned LLama 2 7b language model based on financial datasets, specifically designed for the financial domain, excelling in extracting, understanding, and generating financial-related text.

Financial sentiment classification model based on Deberta-v2 architecture, fine-tuned on multiple financial sentiment datasets

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.

FLANG-BERT is a pretrained language model optimized for the financial domain, based on the BERT architecture, enhanced with a financial term-priority masking strategy to improve domain representation.

A financial sentiment analysis model fine-tuned based on microsoft/deberta-v3-base, specifically designed for analyzing financial news and market sentiment

A financial social media sentiment analysis model fine-tuned on DistilBERT, specialized in analyzing text sentiment from platforms like StockTwits

A sentiment analysis model for stock comments fine-tuned based on the roberta-base model, capable of determining bullish/bearish tendencies in retail investor statements

BERT-based specialized sentiment analysis model for the financial domain, fine-tuned on financial phrase datasets

This model is a financial text sentiment classification model fine-tuned on the financial_phrasebank dataset based on deepmind/language-perceiver.

FinBERT is a BERT model pre-trained on financial communication texts, specializing in the field of financial natural language processing. finbert-tone is its fine-tuned version for financial sentiment analysis tasks.

A pre-trained language model for the Korean financial domain, enhancing financial text processing performance through incremental pre-training and sentiment analysis fine-tuning

BERT-based financial sentiment analysis model trained with noisy search augmentation, suitable for sentiment classification of financial news

FinBERT is a BERT-based model pretrained on financial texts, aimed at advancing research and practice in financial natural language processing.

A financial text sentiment analysis model fine-tuned based on distilroberta-base, used to determine the sentiment tendency (negative, neutral, positive) of financial news.

This model is based on the BERT architecture and fine-tuned for content from the WallStreetBets subreddit, suitable for analyzing financial discussions and sentiments in the forum.

FinBERT is a pre-trained natural language processing model specifically designed for financial text sentiment analysis