NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship πŸ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release πŸŽ‰
Free Lesson
Intro to Data Science New Release πŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See πŸ”₯
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular πŸ”₯ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New πŸŽ‰ Generative AI for Finance New πŸŽ‰ Generative AI for Marketing New πŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular πŸ”₯ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular πŸ”₯ Data Science R: Machine Learning Designing and Implementing Production MLOps New πŸŽ‰ Natural Language Processing for Production (NLP) New πŸŽ‰
Find Inspiration
Get Course Recommendation Must Try πŸ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release πŸŽ‰
Free Lessons
Intro to Data Science New Release πŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See πŸ”₯
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > Data Visualization > Movie Metacritic - Exploring Critics' Movie Reviews

Movie Metacritic - Exploring Critics' Movie Reviews

Hanxiao Zhang
Posted on Dec 24, 2019
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Motivation

Do you check IMDB and Rotten Tomato scores before watching a movie? As a regular moviegoer, I always check critic scores on Metacritic. At the time when I was deciding the topic for this project, two movies on my list caught my eyes: Joker and Parasite. They are both crime movies that are highly rated by the audience. Both scored more than 90 after they took home Top Prize at Film Festivals. However, Joker’s critic score dropped significantly to 59 around its release date, while Parasite's score remains the same.

Considering the difference in score for these two movies made me think of a two key questions:

  • Why do movie scores change overtime and how?
  • Do critics have movie preferences?

This project is intended to answer the questions above by scraping metacritic.com using scrapy and conducting natural language processing (NLP), sentiment and numerical data analysis together with data visualization using Pandas. All Python script and data can be found in my Github repository.

Background: Metacritic and Metascore

Launched in January 2001, Metacritic has evolved over the last decade to distill critics' voices into a single Metascore, a weighted average of the most respected critic reviews online and in print.  Metascores range from 0-100; the higher the score, the better the overall reviews. Metascores are highlighted in three colors below: green for favorable reviews, yellow for mixed reviews, and red for unfavorable reviews.



How To Create A Metascore

Data Scraped

Two separate spiders are implemented to avoid scraping duplicated information for each movie. Spider 1 scraped the first layer along the list of 'Best Movies of All Time',  features including the following:

  • Movies titles
  • Movies genre
  • Distributor
  • Release date
  • Metascore and userscore
  • Number of positive, mixed, negative reviews

Spider 2 goes deeper and scraped each movie’s individual reviews with the following features:

  • Critic’s Name
  • Media Name
  • Critic’s Individual Score
  • Review Date
  • Review Content

NLP and Sentiment Analysis

The word cloud below is derived based on the reviews of good movies (Metascore over 70 ) and bad movies (Metascore below 30) for easy comparison.

The most frequent words used are Character, Story and Director, for both positive and negative reviews.

Left: Metascore > 70 movies Right: Metascore < 30 movies

 

Even though good and bad movie reviews show right and left skew accordingly, most critics choose words and express sentiment in a neutral way.

Blue: Metascore > 70 movies Orange: Metascore < 30 movies

Movie Genre

Reviews for good and bad movies show different movie genre keywords as well.

Drama and documentary are frequently mentioned in positive reviews while comedy, thriller, action and horror movies often are in negative reviews.

Positive Reviews with Metascore > 70
Negative Reviews with Metascore < 30

User scores on Metacritic are used here to better compare critic preference with that of users. In general, user score for each movie genre is higher than Metascore except for 6 genres for which the Metascore averages is 58 with a user average of 67. Metascore also has a higher standard deviation.

Review Date Analysis

By randomly generating movies and their scatter plot and distribution, we can see that reviews are published mainly before and around release date. There are only a few reviews later than that. For most of the movies, their reviews before release date came out on same dates. Consequently, in some plots, we see two or three straight lines. It's highly possible that there are special screening events for the movie before release date and the next date movies reviews from different media come out at the same time.

Score distribution shows that most of the highest scores come before release date, while the reviews tend to be more neutral after the movie is released.

With these observations, let’s go back and see what happened to the reviews for Joker. The same patterns can be found here as well:

Review Dates & Review Score

Tier 1: Venice Movie Festival. The review came out the day after the festival screening with most scores above 60. 

Tier 2: Critic screening before movie release with mixed reviews. 

Tier 3: Around movie release when most review are published, feedback are mixed unlike after movie festivals.

Weighted Average Metascore

At the time I was doing the project, Metascore for Joker was 59, while its equal weighted average is above 59, indicating that the negative reviews in this case have higher weights in the calculating process. The negative reviews came from The New York Times (30), The New Yorker (20 and 30, 2 reviews were published and collected in the same week) ,The WSJ (20), and  Time (20).

Please note that scores are assigned by Metacritic at its own discretion. Some of the conversions are obvious (for example, if a critic uses a 0-10 scale, his/her grade is simply multiplied by ten). Some are less obvious or does not exist at all.

Conclusion

  • Critics favor drama and documentary over comedy, thriller, action and horror movies.
  • Reviews, as a piece of news, are time critical. They come out mainly after special events, such as movie festivals, critic or private screening events before and around the official movie release, which results in Metascore changing over time.
  • Scores are higher before release and more mixed around and after movie release, which results in Metascore decreasing over time for most movies.
  • For Metascore, the media outlet included in the calculating pool and their weights matter.

With this information revealed, we can better understand the score from critic reviews and make the most informed decision possible about the movie we really want to see. 😊

About Author

Hanxiao Zhang

Hanxiao(Mia) Zhang is NYC Data Science Fellow with a Master's Degree in Finance from Fordham University. Before enrolling in the NYCDSA, she worked in the finance and business sector for over 4 years with extensive client interactions on...
View all posts by Hanxiao Zhang >

Related Articles

Capstone
Catching Fraud in the Healthcare System
Data Analysis
Car Sales Report R Shiny App
Data Analysis
Injury Analysis of Soccer Players with Python
Capstone
The Convenience Factor: How Grocery Stores Impact Property Values
Capstone
Acquisition Due Dilligence Automation for Smaller Firms

Leave a Comment

Cancel reply

You must be logged in to post a comment.

No comments found.

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    Β© 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application