Stock Price Metal Sentimental Analysis in Python Projects

Stock Price Metal Sentiment Analysis using Machine Learning and Natural Language Processing (NLP) in Python

Abstract

Stock Price Metal Sentiment Analysis is a Machine Learning and Natural Language Processing (NLP) project that predicts the price movement of metal-related stocks (such as gold, silver, copper, aluminum, and steel companies) by analyzing market sentiment from financial news, social media posts, and historical stock prices. Traditional stock prediction models rely only on historical price data, whereas this project combines technical indicators with sentiment analysis to improve forecasting accuracy.

The system collects historical stock price data and financial news, preprocesses textual and numerical data, extracts sentiment using NLP techniques, and trains machine learning models such as Logistic Regression, Random Forest, XGBoost, LSTM, and BERT-based sentiment models. The final prediction indicates whether the stock price is likely to increase, decrease, or remain stable. The project also provides visualization dashboards showing stock trends, sentiment scores, and prediction results, helping investors make informed investment decisions.

Existing System

Current stock prediction systems mainly use:

Historical stock price analysis
Technical indicators
Manual market research
Expert financial opinions
Fundamental analysis
Disadvantages
Ignores public sentiment
Lower prediction accuracy
Cannot process large volumes of news quickly
Time-consuming analysis
Delayed investment decisions
Limited adaptability to sudden market events
Proposed System

The proposed system integrates stock market data with sentiment analysis from financial news and social media.

The system performs:

Collection of historical metal stock prices
Collection of financial news and tweets
Text preprocessing
Sentiment analysis using NLP
Feature extraction
Machine Learning model training
Stock movement prediction
Visualization of stock trends and sentiment
Advantages
Higher prediction accuracy
Uses real-time sentiment information
Faster market analysis
Better investment decision support
Automatic sentiment classification
Scalable to multiple metal stocks

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