What features are used for sentiment analysis?

The system uses TF-IDF vectorization with 5,000 features and n-gram range (1,2) for text representation. It captures term frequency and inverse document frequency from financial news headlines to create numerical feature vectors.

What is the accuracy of this sentiment analysis model?

The XGBoost model achieves 84.18% test accuracy with a macro F1-score of 0.837, precision of 0.839, and recall of 0.842, making it highly reliable for financial sentiment classification.

Who can benefit from this financial sentiment analysis system?

Traders can use it for market timing decisions, financial analysts for news aggregation, investment firms for algorithmic trading, and researchers for studying market sentiment dynamics.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, XGBoost, Matplotlib, and Seaborn for machine learning. TF-IDF vectorization is used for feature extraction. The web interface is developed using Flask with HTML, CSS, and JavaScript.

Financial News Sentiment and Market Movement Prediction System - Complete Project Demo Video

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Machine Learning

Financial News Sentiment and Market Movement Prediction System

The Financial News Sentiment and Market Movement Prediction System is a comprehensive machine learning system that automatically classifies financial news headlines into three sentiment categories: negative, neutral, and positive. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced algorithms like XGBoost and Logistic Regression with TF-IDF feature extraction to achieve 84.18% accuracy.

The system leverages TF-IDF vectorization with 5,000 features and n-gram range (1,2) for text representation. It provides interactive visualizations, feature importance analysis, model comparison, and real-time sentiment prediction to help traders, analysts, and investors make data-driven decisions based on news sentiment.

Python 3.8+ Machine Learning NLP XGBoost Logistic Regression TF-IDF Scikit-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Class Sentiment Classification
XGBoost (84.18% Accuracy)
Logistic Regression (82.75% Accuracy)
TF-IDF Feature Extraction
Feature Importance Analysis
Real-time Sentiment Prediction
Model Performance Comparison
Interactive Visualizations
5-Fold Cross-Validation
Flask Web Application

Algorithms Used

⚡ XGBoost Classifier

Gradient boosting with 100 estimators, handles non-linear relationships and sparse features

🎯 Accuracy: 84.18%

📊 Logistic Regression

Multinomial regression with L2 regularization, interpretable coefficients

🎯 Accuracy: 82.75%

📝 TF-IDF Vectorizer

Text feature extraction with 5,000 features, n-gram range (1,2)

📊 Features: 5,000

🔍 5-Fold CV

Cross-validation for model stability and generalization

📊 XGB: 83.45% ± 1.34%

Methodology & Workflow

1 Data Loading & Inspection
Financial news dataset with 5,247 headlines

2 Text Preprocessing
Cleaning, lowercasing, special character removal

3 Feature Extraction
TF-IDF vectorization with 5,000 features

4 Model Training
XGBoost & Logistic Regression with optimized parameters

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC, 5-fold CV

6 Web Deployment
Flask web app for real-time sentiment prediction

Model Performance Comparison

Metric	XGBoost	Logistic Regression	Best
Test Accuracy	0.8418	0.8275	XGBoost
Precision (Macro)	0.8383	0.8240	XGBoost
Recall (Macro)	0.8361	0.8193	XGBoost
F1-Score (Macro)	0.8371	0.8216	XGBoost
CV Mean (5-Fold)	0.8345	0.8197	XGBoost
CV Std Dev	0.0134	0.0146	XGBoost

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (5,247 headlines) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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Complete Source Code

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