dixitdharmansh07/indic-finance

# 🇮🇳 Indic-Finance-Sentiment-Hub

A high-frequency, multi-source dataset of Indian financial news, social media discussions, and stock price movements. This dataset is designed for training sentiment analysis models (like FinBERT) and predictive models for the Indian Equity Market (NSE/BSE).

## 📊 Dataset Summary

The dataset aggregates headlines and posts from 150+ top Indian companies (Nifty 50, Nifty Next 50, and major mid-caps). Each observation is labeled with a *FinBERT sentiment score* and linked to the *Target Variable: Forward Return %* (the stock's price change on the next trading day).

### Key Features:
- *Coverage:* Jan 2024 – Present.
- *Sources:* Google News, Economic Times Archive, MoneyControl RSS, LiveMint RSS, Business Standard RSS, Reddit (r/IndianStreetBets), and StockTwits.
- *Ticker Universe:* 150+ NSE-listed companies.
- *Labels:* Sentiment (Positive/Negative/Neutral) + Continuous Forward Returns.

## 🗂️ Dataset Structure

| Column | Description |
| :--- | :--- |
| Publication date of the news/post. |
| headline | The news headline or social media post text. |
| | The NSE symbol (e.g., RELIANCE.NS, ZOMATO.NS). |
| source | The origin of the data (e.g., "economic_times", "reddit"). |
| sentiment_label | The dominant sentiment class (positive, negative, neutral). |
| sentiment_positive | Probability score for positive sentiment (0.0 - 1.0). |
| sentiment_negative | Probability score for negative sentiment (0.0 - 1.0). |
| forward_return_pct | The actual % change in stock price on the next trading day. |
| return_direction | Binary label: 1 if forward_return_pct > 0, else 0. |

## 🚀 Quick Start

You can load this dataset directly using the Hugging Face datasets library:

```python
from datasets import load_dataset

# Replace with your path after uploading
dataset = load_dataset("dixitdharmansh07/indic-finance")

# Convert to Pandas for analysis
df = dataset['train'].to_pandas()
print(df.head())