Economic Times India Headlines Dataset

This dataset includes structured news headline records sourced from the Economic Times covering economic, business, and financial developments in India. Each record typically includes the publication date and headline text, and may include category or section labels depending on the dataset structure. The dataset has undergone a structured exploratory data analysis workflow including duplicate headline removal, date normalization, text encoding validation, and whitespace normalization. Text fields were cleaned to remove malformed characters and ensure UTF-8 compatibility. The final dataset is optimized for natural language processing workflows including sentiment analysis, topic modeling, and time-series text analytics.

Data Quality

The dataset is organized in a structured tabular format with consistent column naming and validated date fields. Duplicate headlines have been removed to preserve textual uniqueness. Headline text has been standardized to remove encoding issues and extraneous characters. Date values have been normalized to maintain chronological integrity. The dataset is suitable for NLP modeling, keyword extraction, and economic sentiment analytics.

Use Cases

This dataset can be used for financial sentiment analysis, news-based trading signal modeling, and economic trend forecasting. Researchers can perform topic modeling and text clustering to analyze economic themes. Data scientists can build NLP models for classification, sentiment scoring, and predictive analytics linking news sentiment with market movements. The dataset also supports dashboard visualization of economic narrative trends over time.

Limitations

The dataset is community-sourced and may be derived from publicly available news headlines. It contains headlines only and does not include full article text. It may not capture all Economic Times publications comprehensively. Legal compliance depends on the scraping and redistribution permissions of the original source. The dataset is intended for research and analytical purposes.

Methodology

The dataset was prepared using a structured validation and normalization framework including schema standardization, duplicate removal, date formatting consistency checks, and text normalization. Character encoding was verified to ensure compatibility with NLP libraries. Basic anomaly detection was applied to remove malformed or empty headlines. The final dataset was exported in UTF-8 encoded CSV format to ensure compatibility with Python NLP frameworks, machine learning pipelines, and business intelligence platforms.