---
task_categories:
- summarization
- text-classification
language:
- en
tags:
- finance
- Financial News
- Sentiment Analysis
- Stock Market
- Text Summarization
- Indian Finance
- BERT
- FinBERT
- NLP (Natural Language Processing)
- Hugging Face Dataset
- T5-base
- GPT (Google Sheets Add-on)
- Data Annotation
pretty_name: IndiaFinanceSent Corpus
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name

<!-- Provide a quick summary of the dataset. -->

The FinancialNewsSentiment_26000 dataset comprises 26,000 rows of financial news articles related to the Indian market. It features four columns: URL, Content (scrapped content), Summary (generated using the T5-base model), and Sentiment Analysis (gathered using the GPT add-on for Google Sheets). The dataset is designed for sentiment analysis tasks, providing a comprehensive view of sentiments expressed in financial news.


## Dataset Description

<!-- Provide a longer summary of what this dataset is. -->



- **Curated by:** Khushi Dave
- **Language(s):** English
- **Type:** Text
- **Domain:** Financial, Economy
- **Size:** 112,293 KB
- **Dataset:** Version: 1.0
- **Last Updated:** 01/01/2024

## Dataset Sources 

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://huggingface.co/datasets/kdave/Indian_Financial_News

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

**Sentiment Analysis Research:** Ideal for exploring sentiment nuances in Indian financial news.

**NLP Projects:** Enhance NLP models with diverse financial text for improved understanding.

**Algorithmic Trading Strategies:** Study correlations between sentiment shifts and market movements.

**News Aggregation:** Generate concise summaries with sentiment insights for financial news.

**Educational Resource:** Hands-on examples for teaching sentiment analysis and financial text processing.

**Ethical AI Exploration:** Analyze biases in sentiment analysis models for ethical AI research.

**Model Benchmarking:** Evaluate and benchmark sentiment analysis models for financial text.

**Note:** Use cautiously; do not rely solely on model predictions for financial decision-making.

## Dataset Creation

- **Format:** String
- **Columns:**
URL: URL of the news article

Content: Scrapped content of the news article

Summary: Summarized version using T5-base

Sentiment Analysis: Sentiment labels (Positive, Negative, Neutral) gathered using the GPT add-on

## Data Collection

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

**Source Selection:** Aggregation of Indian financial news articles from reputable sources covering a range of topics.

**URL Scrapping:** Extraction of URLs for each article to maintain a connection between the dataset and the original content.

**Content Scrapping:** Extraction of article content for analysis and modeling purposes.

**Summarization:** Utilization of the T5-base model from Hugging Face for content summarization.

**Sentiment Annotation:** Manual sentiment labeling using the GPT add-on for Google Sheets to categorize each article as Positive, Negative, or Neutral.

## Data Processing:

**Cleaning and Tokenization:** Standard preprocessing techniques were applied to clean and tokenize the content, ensuring uniformity and consistency.

**Format Standardization:** Conversion of data into a structured format with columns: URL, Content, Summary, and Sentiment Analysis.

**Dataset Splitting:** Given the subjective nature of sentiments, the dataset was not split into training, validation, and testing sets. Users are encouraged to customize splits based on their specific use cases.

## Tools and Libraries:

**Beautiful Soup:** Used for web scraping to extract content from HTML.
**Hugging Face Transformers:** Employed for summarization using the T5-base model.
**GPT Add-on for Google Sheets:** Facilitated manual sentiment annotation.
**Pandas:** Utilized for data manipulation and structuring.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

```bibtex
@dataset{AuthorYearFinancialNewsSentiment_26000,
  author = {Dave, Khushi},
  year = {2024},
  title = {IndiaFinanceSent Corpus},
  url = {[https://huggingface.co/datasets/kdave/Indian_Financial_News]},
}
```


## Dataset Card Authors

Khushi Dave, Data Scientist