Stock Market Sentiment Analysis

Overview

This project aims to analyze financial news articles and predict their sentiment using a pre-trained sentiment analysis model. The sentiment analysis focuses on financial news to help understand market trends and investor sentiment, which can be crucial for decision-making in stock trading and investments.

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Features

• Sentiment Analysis: Predicts whether financial news articles have a positive, neutral, or negative sentiment.
• Model Integration: Utilizes a fine-tuned version of distilroberta optimized for financial sentiment analysis.
• Dataset Support: Processes financial datasets for model training and evaluation.

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Model

We use the DistilRoBERTa model fine-tuned for financial sentiment analysis, published on Hugging Face by mrm8488.
Model link: DistilRoBERTa Fine-tuned Financial News Sentiment Analysis

Why This Model?

This model is specifically fine-tuned to analyze financial texts, making it highly suitable for extracting sentiments from financial news articles, unlike generic sentiment analyzers.

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Datasets

The project uses financial news datasets made available on Hugging Face by NickyNicky.
Dataset link: Financial News Dataset

Dataset Highlights:

• Comprises financial news articles tagged with sentiment labels.
• Suitable for both model training and evaluation.

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• Loss: 0.4090
• Accuracy: 0.9171

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• num_epochs: 5

Training results

Training Loss	Epoch	Validation Loss
0.318500