Inspiration

In a world where a tweet out of context can cost you your career, it is increasingly important to be in the right, but this rigidity alienates a productive and proud group of people in the world--the impulsive. Politically Correct is a solution for those who would risk a slap for a laugh and who would make light of a dark situation.

The question of whether we have stepped too far over the line often comes into our minds, sparking the endless internal debate of "Should I?" or "Should I not?" Politically Correct, leveraging both artificial and natural intelligence, gives its users the opportunity to get safe and effective feedback to end these constant internal dialogues.

What it does

Through a carefully integrated messaging backend, this application utilizes Magnet's API to send the text that the user wants to verify to a randomly selected group of users. These user express their opinion of the anonymous user's statement, rating it as acceptable or unacceptable. This application enhances the user's experience with a seamless graphical interface with a "Feed" giving the user messages from others to judge and "My Questions" allowing users to receive feedback. The machine learning component, implemented and ready to be rolled out in "My Questions", will use an Azure-based logistic regression to automatically classify text as politically correct or incorrect.

How I built it

Blood, sweat, tears, and Red Bull were the fuel that ignited Politically Correct. Many thanks to the kind folks from Magnet and Azure (Microsoft) for helping us early in the morning or late at night. For the build we utilized the Magnet SDK to enable easy in-app messaging and receiving between users and a random sample of users. With the messages, we added and triggered a message 'send-event' based on the click of a judgement button or an ask button. When a message was received we sorted the message (either a message to be judged or a message that is a judgement). To ensure that all judgement messages corresponded to the proper question messages we used special hash ids and stored these ids in serialized data. We updated the Feed and the MyQuestions tab on every message receive.

For Azure we used logistic regression and a looooooooonnnnnnnnggggggg list of offensive and not offensive phrases. Then after training the set to create a model, we set up a web api that will be called by Politically Correct to get an initial sentiment analysis of the message.

Challenges We ran into

Aside from the multiple attempts of putting foot in mouth, the biggest challenges came from both platforms: Azure: Perfectionism While developing a workflow for the app the question of "How do I accurately predict the abuse in a statement?" often arose. As this challenge probably provokes similar doubts from Ph.Ds we would like to point to perfectionism as the biggest challenge with Azure.

Magnet: Impatience Ever the victims, we like to blame companies for putting a lot of words in their tutorials because it makes it hard for us to skim through (we can't be bothered with learning we want to DO!!). The tutorials and documentation provided the support and gave all of us the ability to learn to sit down and iterate through a puzzle until we understood the problem. It was difficult to figure out the format in which we would communicate the judgement of the random sample of users.

Accomplishments that I'm proud of

We are very proud of the fact that we have a fully integrated Magnet messaging API, and the perfect implementation of the database backend.

What I learned

Aside from the two virtues of "good enough" and "patience", we learned how to work together, how to not work together, and how to have fun (in a way that sleep deprivation can allow). In the context of technical expertise (which is what everyone is going to be plugging right here), we gained a greater depth of knowledge on the Magnet SDK, and how to create a work flow and api on Azure.

What's next for Politically Correct

The future is always amazing, and the future for Politically Correct is better (believe it or not). The implementation for Politically Correct enjoys the partial integration of two amazing technologies Azure and Magnet, but the full assimilation (we are talking Borg level) would result in the fulfillment of two goals: 1) Dynamically train the offense of specific language by accounting for the people's responses to the message. 2) Allow integrations with various multimedia sites (i.e. Facebook and Twitter) to include an automatic submission/decline feature when there is a consensus on the statement.

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