Welcome to the AlchemyAPI Twitter analysis recipe!

For the walkthrough on how this software works, visit: http://www.alchemyapi.com/developers/getting-started-guide/twitter-sentiment-analysis/

In short, this recipe will:

• Connect to Twitter's API
• Run a search and gather Tweets
• Enrich Tweets with sentiment analysis of the message body
• Store data in a MongoDB instance
• Visualize sentiment of the data

• For more information on AlchemyAPI's sentiment analysis API: http://www.alchemyapi.com/products/features/sentiment-analysis/
• For more information on Twitter's API: https://dev.twitter.com/rest/public
• For more information on MongoDB: http://www.mongodb.org/
• For more information on R: http://www.r-project.org/

• An API key from AlchemyAPI -- register for one at: http://www.alchemyapi.com/api/register.html
• Credentials for Twitter API -- the Consumer Key and Consumer Secret (create a new application at: https://dev.twitter.com/apps)
• An installation of MongoDB -- http://www.mongodb.org/downloads
• An installation of R, and the "ggplot2" package -- see e.g., http://cran.cnr.berkeley.edu/ and http://www.r-bloggers.com/installing-r-packages/

Here is a simple set of commands that will ensure you have all the necessary tools and modules needed to successfully run this recipe:

sudo easy_install pymongo datetime
sudo apt-get update
sudo apt-get install r-base-dev
sudo R
R> install.packages("ggplot2")
R> <Ctrl-D>
sudo apt-get install mongodb-org
# Starting your MongoDB daemon
# Alternatively, run as root: service mongodb start
mkdir -p data/db
mongod --dbpath data/db --smallfiles --quiet &

To run this tool, simply call with a search term and an integer reflecting the number of Tweets to analyze:

python recipe.py "<SEARCH_TERM>" <NUM_TWEETS>

• Credentials: You must have API credentials for both Twitter and AlchemyAPI to successfully run this tool. Edit the file credentials.py before executing recipe.py to prevent any errors. Inside the file credentials.py, there are three parameters. Replace each of these with your respective values to run the analysis smoothly:

  twitter_consumer_key='YOUR_TWITTER_CONSUMER_KEY' 
  twitter_consumer_secret='YOUR_TWITTER_CONSUMER_SECRET'
  alchemy_apikey='YOUR_ALCHEMY_API_KEY'

• Dependencies (see above for installation tips):

  • pymongo Python module: install with easy_install
  • mongodb: install with apt-get (Ubuntu) or check here: http://docs.mongodb.org/manual/tutorial/install-mongodb-on-ubuntu/

With your MongoDB instance created and filled, you now have access to well-tabulated results! We can proceed with some Python and R scripts to write results to disk and visualize them.

• Write your data: Here, we will write two types of files: one containing the raw scores of sentiment for each Tweet, and one containing timestamps for each Tweet. We will do this separately for positive and negative Tweets. To write these files, just call:

  $> python write.py

  This will result in a total of 4 text files being written: scores.pos, scores.neg, times.pos, and times.neg.

• Dependencies: This tool requires the pymongo Python module, as well as the datetime module (see above).

• Visualize your data: Here, we want to make some plots showing the data we wrote out in the previous steps. To do this, simply invoke the included R script as follows:

  R < plot.R --vanilla

  This tool will write 3 image files to disk:

  • twitter_sentiment_raw.png: a histogram showing the positive and negative Tweet scores
  • twitter_sentiment_kernel.png: a kernel density function corresponding to the above histograms
  • twitter_sentiment_volume.png: a plot showing Tweet volume (separated by sentiment) as a function of the hour in a day

• Dependencies:

  • You must have R installed on your system to run this tool.
  • You must also install the 'ggplot2' library for R (see above).

From time to time, it's good to empty out your cache! If you want to wipe the twitter_db instance in MongoDB that was created in STEP 1, run the following command:

python delete.py