Sentiment Analysis Stock Tweets FineTuned

🚀 vidhivaish03/Sentiment_Analysis_Stock_Tweets_FineTuned

This fine - tuned Transformer - based model is designed for sentiment analysis of stock - related tweets, classifying them into Bearish, Bullish, and Neutral sentiments.

🚀 Quick Start

The vidhivaish03/Sentiment_Analysis_Stock_Tweets_FineTuned model is readily available on the Hugging Face Hub. You can access it via RESTful APIs or integrate it into financial sentiment analysis pipelines for real - time sentiment predictions of stock - related tweets.

✨ Features

• Fine - Tuned Transformer: Based on the [ahmedrachid/FinancialBERT - Sentiment - Analysis](https://huggingface.co/ahmedrachid/FinancialBERT - Sentiment - Analysis) base model, fine - tuned for enhanced performance on financial news tweets.
• Multi - Class Sentiment Analysis: Capable of classifying stock - related tweets into three sentiment categories: Bearish, Bullish, and Neutral.
• Easy Deployment: Available on Hugging Face Hub, facilitating seamless integration into various applications.

📦 Installation

There is no specific installation command provided in the original document. However, you can use the following general steps to use the model from the Hugging Face Hub:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("vidhivaish03/Sentiment_Analysis_Stock_Tweets_FineTuned")
model = AutoModelForSequenceClassification.from_pretrained("vidhivaish03/Sentiment_Analysis_Stock_Tweets_FineTuned")

📚 Documentation

Model Overview

Dataset

• Dataset Name: [zeroshot/twitter - financial - news - sentiment](https://huggingface.co/datasets/zeroshot/twitter - financial - news - sentiment)
• Description: The dataset consists of financial news tweets annotated with three sentiment labels: Bearish, Bullish, and Neutral. It includes 9,543 training samples and 2,388 validation samples.
• Data Structure:
  • Training Set: 9,543 samples
  • Validation Set: 2,388 samples

Training Details

• Preprocessing: Text cleaning (removal of URLs, special characters), tokenization, stop word removal, lemmatization, and vectorization using Word2Vec embeddings.
• Training Framework: Hugging Face's Transformers library
• Fine - Tuning Epochs: 5
• Optimizer: AdamW
• Learning Rate: 2e - 5

Model Performance

The classification report provides a detailed performance overview of the fine - tuned FinancialBERT model on the validation set, evaluating its ability to classify stock - related tweets into Bearish, Bullish, and Neutral sentiments.

Use Cases

• Investor Sentiment Analysis: To gauge market sentiment from social media posts and financial news, aiding investment decisions.
• Market Trend Prediction: To anticipate stock movements based on aggregate sentiment analysis from a variety of sources.
• Financial News Monitoring: To automatically categorize financial news articles and tweets into sentiment categories, streamlining information gathering for analysts.

Limitations

• Domain Specificity: The model is fine - tuned specifically for financial news tweets, and may not generalize well to other domains.
• Context Understanding: While FinancialBERT captures financial context well, it might not interpret nuanced sentiments or sarcasm effectively.
• Data Bias: The model's performance is contingent on the quality and representativeness of the training dataset, which might include biases inherent in social media discussions.

Future Work

• Domain Expansion: Fine - tune additional models for other domains such as general news or consumer reviews.
• Multi - Modal Analysis: Integrate sentiment analysis with other data types like images or financial indicators for more comprehensive insights.
• Enhanced Interpretability: Develop methods to better interpret and explain model predictions to non - technical stakeholders.

Deployment

• Hugging Face Hub: The model is available on Hugging Face Hub for easy integration and deployment.
• Inference: The model can be accessed via RESTful APIs or integrated into financial sentiment analysis pipelines for real - time predictions.

Repository Link: vidhivaish03/Sentiment_Analysis_Stock_Tweets_FineTuned

This model card summarizes the key aspects of vidhivaish03/Sentiment_Analysis_Stock_Tweets_FineTuned, providing insights into its training, performance, and applications.