Create README.md

README.md

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+---
+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