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CS122-Project, Very Fun Tweets

Chelsea Ernhofer, Sushmita Gopalan, and Haylee Ham

www.veryfuntweets.com

Index of Files

  • streaming_api.py
    • Connects to Twitter API, streams sample of tweets in mongo database
    • Constantly running on RCC servers
  • spikes.py
    • Detects spikes in hashtag usage- up to 3 per hashtag
  • scraping folder
    • tweet_scraper.py - calculates most common words in tweet text
    • news_scraper.py - finds relevant news stories by date & hashtag
  • whole_rcc.py
    • main script used to pass data from mongo, through spikes and scraping scripts, and through to postgres
  • push_to_postgres.py
    • transfers data from whole_rcc.py into postgres database, in proper format
  • django_app
    • contains all necessary files to run django application
    • includes views, templates, and models

Data Collection

This project uses MongoDB to collect a sample of data from Twitter using the streaming API. Here are the steps to set up a MongoDB instance and start collecting Twitter Data.

  • Make sure you have Mongo installed (brew install mongo)
  • Create a Mongo database called cs122
  • Put Twitter API keys in a .env (ACCESS_TOKEN, ACCESS_TOKEN_SECRET, CONSUMER_KEY, CONSUMER_KEY_SECRET)
    • You can supply a valid MongoDB URI in place of creating the MongoDB locally (MONGODB_URI)
  • Run pip3 install -r requirements.txt
  • Run python3 streaming_api.py

This will start feeding tweets into your MongoDB. A random sample of 1% of all English tweets will start streaming.

Django App

We have created a Django app that makes visualizations to understand spikes in hashtag usage and the news stories that give context to those spikes. Before we can transfer the data from our large MongoDB instance, we need to set up Django app.

  • Make sure you have postgres installed (brew install postgres)
    • The database should be named cs122 unless provided a valid postgres URI in the .env as DATABASE_URL
  • Run the app locally with python3 django_app/tweets/manage.py runserver

Once the app is set up, we can start moving data from the large MongoDB into a more consumable format that Django has access to using our provided scripts which tease out spikes and scrape google news for context. When spikes and stories are found, the corresponding data will be pushed to a PostgresDB to be accessed by the Django app. In order to run these scripts:

  • Make sure you have the correct REQUEST_BASE set in push_to_postgres.py (it will be localhost:8000 when running locally)
  • Run python3 whole_rcc.py

Useful commands for RCC and Heroku environments

Running on Heroku

  • Command to creat a worker dyno and start the stream
    • heroku ps:scale worker=1
  • Command to stop the script from Terminal
    • heroku ps:scale worker=0
  • View processes with heroku ps and logs with heroku logs

Getting set up with streaming on RCC

To run mongo, use the run_mongo alias (if it exists) or ~/bin/mongodb/bin/mongod --dbpath ~/data/db

  • source ~/.bash_profile
  • run_mongo
  • module load python/3.4-2015q1
  • pip3 install -r requirements.txt --user
  • streaming_api.py

Checking up on MongoDB in RCC

  • ~/bin/mongodb/bin/mongo
  • show dbs
  • use cs122
  • db.tweets.count()
  • db.tweets.find().limit(1)

To run migrations

  • python3 manage.py makemigrations
  • python3 manage.py migrate
  • To check: psql cs122
  • To run migrations to Heroku: heroku run python3 django_app/tweets/manage.py migrate

Checking up on Postgres in Heroku

  • heroku run django_app/tweets/manage.py shell
  • from tweet_search.models import Hashtag
  • Hashtag.objects.count()

Deleting data from Postrgres on Heroku

  • heroku run django_app/tweets/manage.py shell
  • from tweet_search.models import Hashtag
  • Hashtag.objects.all().delete()