Entertainment Critic & User Scores Dataset

Columns

• title: The name of the television programme.
• rank: The programme's ranking.
• critic_meta_score: The Metacritic score assigned by an individual author.
• meta score made by each author: The Metacritic score made by each author.
• review_content: The full text of the critic's review.
• review_source: The publication or source of the review (e.g., Variety, The Hollywood Reporter).
• author: The name of the review author.
• review_date: The date the review was published.
• summary: A brief summary of the review content.
• summary of tv show: A brief summary of the television programme.
• meta_score: The overall Metacritic score for the TV programme.
• user_score: The overall user score for the TV programme.

Distribution

• The rank column includes 3136 unique values. Value counts are distributed across various ranges, with counts per range varying from 2,360 to 2,896.
• The meta_score column has 100 unique values. Score distribution shows significant concentrations in certain ranges, for example, 10,507 entries fall within the 80.00 - 85.00 range, and 6,340 entries in the 50.00 - 55.00 range.
• The user_score column has 9.7 unique values. User scores are widely distributed, with the highest counts observed between 7.30 - 7.78 (7,791 entries) and 7.78 - 8.26 (6,919 entries).
• Key review sources include Variety (5% of reviews) and The Hollywood Reporter (4%), with 91% originating from other sources.
• Prominent review authors include Brian Lowry (4%) and Rob Owen (3%), with 93% attributed to other authors.
• Review dates span from 20th October 2010 to 11th June 2022. The volume of reviews varies over time, with notable peaks such as 4,556 reviews between February 2013 and September 2013.

Usage

• Analysing trends in television programme reception over time.
• Developing natural language processing (NLP) models for sentiment analysis on reviews.
• Comparing critic and user opinions on TV shows.
• Market research into audience preferences and critical success factors in entertainment.
• Building recommendation systems for TV content.

Coverage

License

Who Can Use It

• Data Scientists and AI/ML Developers: For training models on textual data (reviews), sentiment analysis, and predictive analytics related to media consumption.
• Media and Entertainment Analysts: To gain insights into industry trends, programme performance, and audience engagement.
• Researchers: For academic studies on media criticism, cultural impact of television, and public opinion.
• Product Managers and Content Creators: To understand what drives positive reception for TV shows.

Dataset Name Suggestions

• TV Programme Reviews Metacritic
• Metacritic TV Show Ratings
• Historical TV Reviews Dataset
• Entertainment Critic & User Scores

Attributes