Brightening days through ML
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.gitignore
HAN.py
README.md
classify.txt
encouraging.txt
gettweets.py
index.py
nlp1.py
pic1.png
pic2.png
pic3.png
posts.txt
requirements.txt

README.md

logo

Counteract

Welcome to the repository for Counteract, a crowdsourced risk analysis platform for sending nice messages to people who may be having a bad day or show signs of threatening behavior to themselves or others.

Info:

We utilized (Hierarchical Attention Networks)[https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf] for classification on public tweets to classify tweets as happy/sad, then a Markov chain on encouraging tweets to generate a happy message to tweet at the person who tweeted something sad.

Demo:

The following tweets are 100% generated through our system with no human interference :) pic1 pic2 pic3

File Importance:

  • posts.txt - A list of tweets we mined with a tweet on each line

  • classify.txt - A list of classifications corresponding to posts.txt that we classified as happy/sad

  • encouraging.txt - A list of encouraging messages/quotes that we used to train the Markov chain

  • requirements.txt - A list of Python requirements required to run our project, along with Scikit-Learn and Numpy

  • gettweets.py - Our tweet mining file that saved the tweets into posts/classify.txt

  • nlp1.py - Our original text classification engine courtesy of Patrick Demichele

  • index.py - Our main Python file that streams tweets and generates responses to the sad tweets.

Contributors

The following individuals are currently contributing to Counteract: Jay Shenoy, Andy Kamath, Patrick DeMichele, Karan Kanwar, Kelsey Nieman, Matt Zhou, Michelle Huang, and Rufus Behr.