To Run: Create the virtual environment:

python3 -m venv env

Activate the virtual environment:

source env/bin/activate

Install the requirements:

pip install -r requirements.txt

or

pip3 install -r requirements.txt

To Configure the mocks edit the .env file By default the mocks are set to the following:

MOCK_AWARD_CATEGORIES=False
MOCK_AWARD_PRESENTERS=False
MOCK_AWARD_WINNERS=False
MOCK_AWARD_NOMINEES=False
MOCK_HOSTS=False
MOCK_RED_CARPET=False
MOCK_SENTIMENT=False

Turning these on will enable the mocks and will use the data from gg_apifake.py

To Run the program: Supplying no arguments will run the program with the default values You need supply --output_results in order to get console output results Adding --save_json will save the json files to gg_{year}_generated_answers.json in the format for the auto grader

# This will print the results and save the json files
python Runner.py --output_results --year 2013 --save_json

# This will save the json files
python Runner.py --year 2013 --save_json

# This will print the results
python Runner.py --output_results --year 2013

In order to scrape our data from IMDB, we made a file scraper.py

The requirements for the scraper are installed with the requirements.txt file

• - Hosts
• - Award Categories
• - Presenters (not 100% accurate)
• - Nominees (not 100% accurate)
• - Winners

• - Red Carpet
  • Best Dressed
  • Worst Dressed
  • Most Controversial
  • Three Most Discussed
• - Sentiment Analysis
  • Sentiments regarding hosts
  • most positive winner
  • least positive winner

• Our code groups together similar awards into one award name with a set of aliases. These can be found in award_aliases.json. Because of this, it is possible that code may not work will all the subparts when mocking Award Categories.
• The files TimeToJson.py and IntervalTester.py create plots in the folder test_tweets_time/ that provide interesting visualizations about where tweets about certain awards fall on a chronological scale (we used these to identify presenters and nominees)
• The files in saved_jsons/ are for internal use of the award category recognition function.
• The main Runner takes on average 4.5 minutes to run on a Macbook Pro M1 chip (with video/other programs running in the background). The extra sections add around 30 seconds in total.