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--- |
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task_categories: |
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- summarization |
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- text-classification |
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language: |
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- en |
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tags: |
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- finance |
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- Financial News |
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- Sentiment Analysis |
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- Stock Market |
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- Text Summarization |
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- Indian Finance |
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- BERT |
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- FinBERT |
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- NLP (Natural Language Processing) |
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- Hugging Face Dataset |
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- T5-base |
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- GPT (Google Sheets Add-on) |
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- Data Annotation |
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pretty_name: IndiaFinanceSent Corpus |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Dataset Card for Dataset Name |
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<!-- Provide a quick summary of the dataset. --> |
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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. |
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## Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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- **Curated by:** Khushi Dave |
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- **Language(s):** English |
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- **Type:** Text |
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- **Domain:** Financial, Economy |
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- **Size:** 112,293 KB |
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- **Dataset:** Version: 1.0 |
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- **Last Updated:** 01/01/2024 |
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## Dataset Sources |
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<!-- Provide the basic links for the dataset. --> |
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- **Repository:** https://huggingface.co/datasets/kdave/Indian_Financial_News |
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## Uses |
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<!-- Address questions around how the dataset is intended to be used. --> |
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**Sentiment Analysis Research:** Ideal for exploring sentiment nuances in Indian financial news. |
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**NLP Projects:** Enhance NLP models with diverse financial text for improved understanding. |
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**Algorithmic Trading Strategies:** Study correlations between sentiment shifts and market movements. |
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**News Aggregation:** Generate concise summaries with sentiment insights for financial news. |
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**Educational Resource:** Hands-on examples for teaching sentiment analysis and financial text processing. |
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**Ethical AI Exploration:** Analyze biases in sentiment analysis models for ethical AI research. |
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**Model Benchmarking:** Evaluate and benchmark sentiment analysis models for financial text. |
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**Note:** Use cautiously; do not rely solely on model predictions for financial decision-making. |
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## Dataset Creation |
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- **Format:** String |
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- **Columns:** |
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URL: URL of the news article |
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Content: Scrapped content of the news article |
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Summary: Summarized version using T5-base |
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Sentiment Analysis: Sentiment labels (Positive, Negative, Neutral) gathered using the GPT add-on |
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## Data Collection |
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> |
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**Source Selection:** Aggregation of Indian financial news articles from reputable sources covering a range of topics. |
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**URL Scrapping:** Extraction of URLs for each article to maintain a connection between the dataset and the original content. |
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**Content Scrapping:** Extraction of article content for analysis and modeling purposes. |
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**Summarization:** Utilization of the T5-base model from Hugging Face for content summarization. |
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**Sentiment Annotation:** Manual sentiment labeling using the GPT add-on for Google Sheets to categorize each article as Positive, Negative, or Neutral. |
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## Data Processing: |
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**Cleaning and Tokenization:** Standard preprocessing techniques were applied to clean and tokenize the content, ensuring uniformity and consistency. |
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**Format Standardization:** Conversion of data into a structured format with columns: URL, Content, Summary, and Sentiment Analysis. |
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**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. |
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## Tools and Libraries: |
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**Beautiful Soup:** Used for web scraping to extract content from HTML. |
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**Hugging Face Transformers:** Employed for summarization using the T5-base model. |
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**GPT Add-on for Google Sheets:** Facilitated manual sentiment annotation. |
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**Pandas:** Utilized for data manipulation and structuring. |
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## Citation |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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```bibtex |
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@dataset{AuthorYearFinancialNewsSentiment_26000, |
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author = {Dave, Khushi}, |
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year = {2024}, |
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title = {IndiaFinanceSent Corpus}, |
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url = {[https://huggingface.co/datasets/kdave/Indian_Financial_News]}, |
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} |
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``` |
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## Dataset Card Authors |
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Khushi Dave, Data Scientist |
