joyjitroy/Stock_Market_News_Sentiment_Analysis

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📰 NLP Stock Sentiment Analysis — Embedding-Based Models for Market Signals

This model card documents the resources and workflow described in the paper:

“NLP Stock Sentiment Analysis: A Comparative Embedding-Based Model Report” Joyjit Roy SSRN, August 2024 Available at: https://ssrn.com/abstract=5784922 DOI: http://dx.doi.org/10.2139/ssrn.5784922

👥 Authors

Zenodo DOI: https://doi.org/10.5281/zenodo.17510735 GitHub: https://github.com/joyjitroy/MachineLearning/tree/main/NLPStockSentimentAnalysis

This project provides a complete NLP workflow for classifying the sentiment of headline-level financial news and aggregating weekly sentiment indicators. It serves as a reproducible reference for embedding-based sentiment analysis using classical machine learning models.

🔍 Project Overview

The goal is to transform unstructured financial news into structured sentiment scores that can support market commentary and trend evaluation. Three embedding-based approaches were explored:

1. Word2Vec (300D)

• —Trained locally on the news corpus
• —Mean pooled per headline

2. GloVe (100D)

• —Pretrained vectors
• —Mean pooled per headline

3. SentenceTransformer Embeddings (384D)

• —Model: all-MiniLM-L6-v2
• —Direct sentence embeddings without fine-tuning

For each embedding approach, a Gradient Boosting classifier is trained and evaluated on the same dataset. Weekly sentiment summaries are generated to demonstrate applied use cases for financial analysis.

Note: These models are scikit learn models and are not directly deployed as Hugging Face inference endpoints.

💽 Dataset Summary

The dataset contains 349 financial news headlines, each paired with OHLCV market indicators and a sentiment label: 1 = positive, 0 = neutral, -1 = negative.

The dataset is available on Hugging Face and also archived on Zenodo for citation and long-term access: 👉 https://doi.org/10.5281/zenodo.17510735

📊 Data Dictionary

📊 Model Performance (Validation)

Across all embedding variants, the tuned GloVe + Gradient Boosting model achieved the strongest validation results:

• —Accuracy: 0.714
• —Precision: 0.758
• —Recall: 0.714
• —F1 Score: 0.694
• —Error Rate: 0.286

Training accuracy is perfect for all models due to dataset size; validation metrics are therefore used for fair comparison. Full results appear in the associated paper (Table 3).

🧠 Weekly Sentiment Summaries

To show how sentiment predictions can support market interpretation:

1. 1.Daily predictions are aggregated by week
2. 2.Sentiment ratios (positive / neutral / negative) are computed
3. 3.Weekly summaries are generated using Mistral-7B-Instruct
4. 4.These summaries provide narrative insight into market mood

This illustrates a practical workflow where classical NLP models feed into downstream financial analysis.

🌟 Intended Use

This repository is intended for:

• —Research on embedding-based sentiment classification
• —Educational exploration of Word2Vec, GloVe, and Sentence Transformer embeddings
• —Demonstrating weekly sentiment aggregation
• —Benchmarking classical ML approaches for small financial text datasets

⚠️ Limitations

• —Small dataset (349 samples)
• —Potential overfitting of classical models
• —Not designed for automated trading or real-time systems
• —Weekly summaries rely on LLM outputs and may include stylistic bias
• —No direct price prediction or financial forecasting

This model is best used for experimentation and learning. ---

📄 Related Publication

The full experimental design, results, and limitations are documented in the following model report:

Roy, Joyjit. NLP Stock Sentiment Analysis: A Comparative Embedding-Based Model Report. SSRN, 2024. https://ssrn.com/abstract=5784922 DOI: http://dx.doi.org/10.2139/ssrn.5784922

📘 Citation

If you use this model, dataset, or workflow, please cite:

Model Report

Roy, Joyjit. NLP Stock Sentiment Analysis: A Comparative Embedding-Based Model Report. SSRN, August 2024. https://ssrn.com/abstract=5784922 DOI: http://dx.doi.org/10.2139/ssrn.5784922

Dataset

Roy, Joyjit. Stock Market News Sentiment Analysis – Supplementary Materials. Zenodo, 2025. https://doi.org/10.5281/zenodo.17510735