Combines hand-curated seating data from Fandango with Twitter Sentiment analysis though Azure HDInsight

1. Decision Support for
Movie Brokers
DATAVIS 300B Final Project
Leo Salemann
8/29/17

2. Background (What’s a Movie Broker?)
Film Maker
Creates,
Sells Rights
Production Company
Buys Rights,
Distributes
Movie Broker
Negotiates
Leasing Terms
Theater
Leases,
Exhibits
Audience
Watches
The Movie Broker …
• Represents a chain of local theaters.
• Acquires screening rights from Hollywood distributors.
• Identifies which movies to buy next.
• Negotiates screening terms
• Typically the Production Company gets most of the
revenue for weeks 1-2
• Theaters start seeing per-ticket profit week 3 and beyond.
Source: https://www.premiumbeat.com/blog/basics-of-film-distribution/
PS: Now would be a good time to load the finished dashboard into a browser (below) then return to the slides.
http://leos-uw-datavis.s3-website-us-west-2.amazonaws.com/

3. User Persona
Personal Background
• Huge movie buff, wanted to write or direct.
• More about numbers than creativity, found
a way to capitalize on passion.
• Tech-Aware, but not a developer
• Age: 40; Married, 1 child in College
• Education: Undergraduate
• Job Experience: Mid-career, 15 years in
theater management; 10 years as a buyer.
Max the Movie Broker
Goal: Get the best deal for my theater owners
• Fill those seats (empty seats never buy concessions)
• Find the "sleeper hits" that keep selling after the
2nd week (80-90% of revenue goes to distributor for
first 2 weeks)
Frustrations: So much info from so many places
• Critics' reviews
• Movie Revenues (gross $$)
• Theater Revenues (seats sold)
• Social Media
• Which Movies, Genres, Actors are popular?
• What's trending? What's on its way out?
"I have to go to so many
places to sweep up all the
info I need to do my job"
Scenarios
1) Which movies have been doing well nationwide?
2) What's happening at my client's theaters?
3) What are audiences saying?

4. Workflow

5. Capture
Count seats sold on
fandango, record in
spreadsheet by hand
Use PowerBI’s web scraper, aim
at www.boxofficemojo.com
Deploy Azure Time Series Insights from Cortana
Intelligent Solutions, set the Twitter Topics

6. Curate
Organize seats-
sold in Excel
Merge seats-sold,
multiple weekend
box office results,
Tweet Score in
PowerBI
Cortana Intelligence Solution runs tweets
through a sentiment analyzer in Azure ML

7. Scenario 1
Which
movies have
been doing
well? Strong in week 3: Good for Theaters
Strong in week 1: Good for Production Companies

8. Scenario 2
What's
happening at
my client's
theaters?
Average Per-Theater revenue
(bars) remained constant for the
mix of films shown at Regal
Crossroads 8, yet per-seat sales
(line) dropped significantly.
Crossroads revenue probably
lags behind national average
for August 11, 2017.
The companion “Seats Sold,
Weeks Since Release” bar chart
shows how many seats were for
movies in their 1st, 2nd, 3rd …
week of release

9. Scenario 3
What are
audiences
saying?
Dunkirk dominates the Twitterverse, gathering the most tweets, most of which are positive. Detroit
doesn’t have many so far, but has been trending positive. Annabelle has a lot of negative tweets. Is
this because people don’t like it, or is the Sentiment Analyzer assuming phrases like “freaked me
out” and “horrifying” are negative, even when describing a horror movie?

10. Putting
it all
together
See it live at http://leos-uw-datavis.s3-website-us-west-2.amazonaws.com/

11. Future Work
• Support “Future Sate” in addition to “Current State”
• Focus on movies 3-6 months out
• Critic Scores & Fan ratings
• Apply some Machine Learning to forecast 3rd-week sales
based on Genre, Actor, Production Company , ..
• More Social Medial Analysis
• Refined sentiment analysis
• Facebook, other social platforms
• Show Dashboard to actual Movie Brokers

12. Want to Keep your Local Theater in Business?
Watch movies 3 weeks after
release or …
1
Celebrate opening night with a
trip to the concession stand
2

13. Sources
• Box Office Mojo Weekend Charts
• Time Series Insights Using Twitter
• Fandango, Regal Crossroads Stadium 8
• Fandango, AMC Loews Factoria 8
• Max’s Face
• Rotten Tomatoes (movie posters)
• IMDb (more movie posters)

14. Image Sources, Details

15. APPENDIX:
What’s Under the Hood

16. [Future]
Curate
Sentiment
Issues
“Chills” interpreted as
negative for a Horror
movie

17. [Future]
(re)Train the
Binary
Classification
Model

18. Event Hub
ingest

19. Event Hub
process

20. App Service
Plan

21. SQL
Database

22. Time Series
Insight

23. Stream
Analysis Job