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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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pipeline_tag: text-classification |
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library_name: sklearn |
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tags: |
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- finance |
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- sentiment-analysis |
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- embeddings |
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- gradient-boosting |
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- classical-ml |
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- market-analysis |
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- nlp |
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- weekly-sentiment |
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--- |
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<p align="left"> |
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<a href="https://github.com/joyjitroy/Machine_Learning/tree/main/NLP_Stock_Sentiment_Analysis"> |
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<img src="https://img.shields.io/badge/GitHub-Repo-blue?logo=github" /> |
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</a> |
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<a href="https://doi.org/10.5281/zenodo.17510735"> |
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<img src="https://img.shields.io/badge/Zenodo-DOI-1877f2?logo=zenodo" /> |
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</a> |
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<a href="https://huggingface.co/joyjitroy/Stock_Market_News_Sentiment_Analysis"> |
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<img src="https://img.shields.io/badge/HuggingFace-Model-yellow?logo=huggingface" /> |
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</a> |
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<a href="#"> |
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<img src="https://img.shields.io/badge/arXiv-Active-blue?logo=arxiv" /> |
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</a> |
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<a href="#"> |
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<img src="https://img.shields.io/badge/License-MIT-green" /> |
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</a> |
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</p> |
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# 📰 NLP Stock Sentiment Analysis — Embedding-Based Models for Market Signals |
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This model card documents the resources and workflow described in the paper: |
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**“NLP Stock Sentiment Analysis: A Comparative Embedding-Based Approach” |
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(Preprint submitted to arXiv, 2025)** |
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Author: **Joyjit Roy** |
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Zenodo DOI: https://doi.org/10.5281/zenodo.17510735 |
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GitHub: https://github.com/joyjitroy/Machine_Learning/tree/main/NLP_Stock_Sentiment_Analysis |
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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. |
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--- |
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## 🔍 Project Overview |
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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: |
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### **1. Word2Vec (300D)** |
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- Trained locally on the news corpus |
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- Mean pooled per headline |
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### **2. GloVe (100D)** |
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- Pretrained vectors |
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- Mean pooled per headline |
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### **3. SentenceTransformer Embeddings (384D)** |
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- Model: `all-MiniLM-L6-v2` |
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- Direct sentence embeddings without fine tuning |
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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. |
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> Note: These models are scikit learn models and are not directly deployed as Hugging Face inference endpoints. |
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--- |
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## 💽 Dataset Summary |
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The dataset contains **349 financial news headlines**, each paired with OHLCV market indicators and a sentiment label: |
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`1` = positive, `0` = neutral, `-1` = negative. |
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Dataset is available via Zenodo: |
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👉 https://doi.org/10.5281/zenodo.17510735 |
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### 📊 Data Dictionary |
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| Column | Description | |
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|----------|---------------------------------------------------------------| |
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| `Date` | Date the news item was released | |
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| `News` | Headline or snippet text | |
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| `Open` | Opening price (USD) | |
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| `High` | Highest price (USD) of the day | |
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| `Low` | Lowest price (USD) of the day | |
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| `Close` | Adjusted closing price (USD) | |
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| `Volume` | Total shares traded | |
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| `Label` | Sentiment (`1`=positive, `0`=neutral, `-1`=negative) | |
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--- |
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## 📊 Model Performance (Validation) |
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Across all embedding variants, the **tuned GloVe + Gradient Boosting** model achieved the strongest validation results: |
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- **Accuracy:** 0.714 |
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- **Precision:** 0.758 |
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- **Recall:** 0.714 |
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- **F1 Score:** 0.694 |
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- **Error Rate:** 0.286 |
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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). |
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--- |
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## 🧠 Weekly Sentiment Summaries |
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To show how sentiment predictions can support market interpretation: |
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1. Daily predictions are aggregated by week |
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2. Sentiment ratios (positive / neutral / negative) are computed |
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3. Weekly summaries are generated using **Mistral-7B-Instruct** |
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4. These summaries provide narrative insight into market mood |
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This illustrates a practical workflow where classical NLP models feed into downstream financial analysis. |
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--- |
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## 🌟 Intended Use |
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This repository is intended for: |
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- Research on embedding-based sentiment classification |
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- Educational exploration of Word2Vec, GloVe, and Sentence Transformer embeddings |
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- Demonstrating weekly sentiment aggregation |
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- Benchmarking classical ML approaches for small financial text datasets |
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--- |
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## ⚠️ Limitations |
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- Small dataset (349 samples) |
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- Potential overfitting of classical models |
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- Not designed for automated trading or real-time systems |
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- Weekly summaries rely on LLM outputs and may include stylistic bias |
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- No direct price prediction or financial forecasting |
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This model is best used for experimentation and learning. |
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--- |
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## 📘 Citation |
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If you use this work, please cite: |
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> Roy, Joyjit. “NLP Stock Sentiment Analysis: A Comparative Embedding-Based Approach.” arXiv preprint, 2025. |
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> Dataset: Joyjit Roy. “NLP Stock Sentiment Analysis — AI for Market Signals.” Zenodo. https://doi.org/10.5281/zenodo.17510735 |
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