NLP-Based Stock Market Sentiment Analysis System: Predicting Market Trends from Financial News

This article introduces the stock market sentiment analysis system published by rakeshricky442 on GitHub. Its core is to use natural language processing (NLP) and machine learning techniques to predict market trends from financial news headlines. The system covers text preprocessing, bag-of-words model feature extraction, and the implementation and comparison of classification models such as logistic regression, random forest, and naive Bayes, providing a new tool for quantitative trading and risk management.

• Original Author: rakeshricky442
• Source: GitHub
• Release Date: 2026-05-25
• Original Link: https://github.com/rakeshricky442/stock-market-sentiment-analysis

In financial markets, sentiment is an important force driving price fluctuations. Traditional technical analysis and fundamental analysis struggle to capture the psychological changes of participants. With the development of NLP technology, extracting sentiment signals from massive texts such as financial news and social media provides a new perspective for quantitative trading and risk management.

1. Data Collection and Preprocessing

The input is financial news headlines, which require text cleaning (removing special characters), case unification, word segmentation, stopword filtering, and stemming/lemmatization.

2. Feature Extraction: Bag-of-Words Model

Convert text into an unordered set of words, count word frequencies to generate sparse vectors, ignore grammar but retain key sentiment signals.

3. Model Training

Implement three classification algorithms:

• Logistic Regression: Simple and fast, strong interpretability
• Random Forest: Captures non-linear relationships, resists overfitting
• Naive Bayes: Efficient, suitable for small-scale data

4. Model Evaluation

Evaluate using accuracy and confusion matrix to identify the model's optimistic/pessimistic tendencies.

The project uses Python ecosystem libraries (such as scikit-learn). The bag-of-words model is implemented via CountVectorizer (word frequency) or TfidfVectorizer (term frequency-inverse document frequency weighting). The dataset needs to be divided into training/validation/test sets, and cross-validation improves the reliability of evaluation.

• Quantitative Trading Strategy: Use sentiment signals as factors—negative sentiment triggers position reduction, positive sentiment captures upward opportunities
• Risk Management: Real-time monitoring of sentiment changes to identify potential risks in advance
• Event-Driven Analysis: Quickly evaluate market reactions to events such as financial reports and policies

The bag-of-words model cannot capture semantic relationships and context. Improvement directions include:

• Word2Vec/GloVe word vectors: Capture semantic similarity
• RNN/LSTM: Handle sequence dependencies
• Transformer (e.g., BERT, FinBERT): Deep semantic understanding
• Specialized pre-trained models for the financial domain to improve performance

This project demonstrates the application of classical machine learning in the financial field, and its complete pipeline is suitable for introductory learning. Understanding the principles of basic methods is a prerequisite for building complex systems, and deep learning technologies will continue to improve the accuracy and practicality of sentiment analysis.