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Twitter Sentiment

Take a tweet, extract a TON of information out of a short bit of text. Let's do this.

Get it running

1. Install RVM (if you have it already, skip to step 2)


bash -s stable < <(curl -s
source ~/.bash_profile

####Linux Users

rvm install 1.9.3

####Mac Users Install XCode from the App Store if you haven't already (it's free).

rvm install 1.9.3 --with-gcc=clang

OS X users may be interested in Jewelry Box, a Cocoa UI for RVM.

####Windows users Install Ruby from RubyInstaller

The DevKit is also required to install some of the gems described below

2. Install the necessary gems

gem install bundler
bundle install

3. Run it

bundle exec ./app.rb

Directory Structure

lib/ is where the bulk of the code lies. It is all of the library files used by our app.rb.

  • twitter-sentiment/: libraries in our namespace.

    • input/: libraries that contact the outside world via APIs (generally).

    • output/: libraries that send data outward.

    • parser/: libraries that get data form inputs, parse them, and give weights to be aggregated.

    • prefs/: preferences and constants to be used by any of the aforementioned libraries/files.

dict/ contains the collection of dictionaries (bag of words, or BoW) being used for sentiment analysis.

research/ is a general placeholder for interesting papers and potential BoWs.

Research Leads



AFINN: A new word list for sentiment analysis

Simplest Sentiment Analysis in Python


Twitter Mood

ANEW Sentiment-Weighted Word Bank

Measuring User Influence in Twitter


Sentiment strength detection in short informal text

Twitter as a Corpus for Sentiment Analysis and Opinion Mining

We Feel Fine

Modeling Statistical Properties of Written Text (lookup!)