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Motivational Saying Popularity Metrics
Data Science and Analytics
Tags and Keywords
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Free
About
This collection features motivational and inspirational quotes sourced from the Goodreads platform. It was originally compiled to explore what truly inspires people and to identify common themes, authors, and popularity metrics associated with successful quotes. The material provides insight into patterns of inspiration and is suitable for detailed text analysis and exploratory data work.
Columns
- index: An identifying numerical index for each record.
- quote: The full text content of the saying. This column has nearly 3,000 unique values.
- author: The individual credited with the quote. The data includes 1,412 unique authors, with Roy T. Bennett being the most frequently represented.
- tags: Associated thematic keywords, delimited by a semicolon (;). The dominant tag is 'inspirational'.
- likes: A measure of the quote's popularity, reflecting the number of likes it had gathered, with values ranging from 23 up to 149,000.
Distribution
The information is delivered as a CSV file named
quotes.csv, approximately 704.05 kB in size. The structure includes 5 columns and holds 3,001 valid records. All records contain author, tag, and like data, though a small count of five quotes is missing text content.Usage
Ideal applications include conducting natural language processing (NLP) for text mining, performing exploratory data analysis on quote popularity, and visualizing relationships between authors and thematic tags. Users can employ this dataset to determine patterns associated with writing popular motivational content and for general literary studies.
Coverage
The scope is derived exclusively from quotes scraped from the Goodreads website. The data provides a static snapshot of quote content and popularity metrics, as the expected update frequency is 'never'. Geographic or specific temporal coverage details are not relevant, as the dataset focuses purely on text content, authorship, and popularity.
License
CC0: Public Domain.
Who Can Use It
- Data Scientists and Analysts: For training NLP models, text mining, and performing detailed statistical analysis of textual popularity.
- Content Strategists: To understand the structure, themes, and authors whose quotes resonate most effectively with an audience.
- Researchers: For studies in literature and digital humanities focusing on sources of public inspiration and motivational text.
Dataset Name Suggestions
- Goodreads Inspirational Quotes Corpus
- Motivational Saying Popularity Metrics
- Curated Quotes for Text Analysis
Attributes
Original Data Source:Motivational Saying Popularity Metrics
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