Yearning to arrive at a problem that crosses multiple hackathon themes, that we understand, and that would impact society largely left us thinking hard and long on Friday night because we simply did not have much research, experience, or knowledge in these fields...
How would we create a solution to a problem we don’t understand?
This question sparked the idea of using natural language processing to analyze frustrations, complaints, and praises -- a sort of human centered design! We began designing a way to provide hackathoners and businesses with problem statements so they can focus on what they do best.
The time was ticking on the first night as we wrote up user stories to achieve the critical part of that solution: analyzing broken English and
So we sought to create an MVP that would challenge us to grow in skills and frameworks we have not learned yet while also creating something useful to @jetBlue and our aim of machinery for human-centered design. So we developed the Business Brand Barometer V1.0!
jetBlue challenge: Hypothesis: Aggregating top-ranked complaints from social media will point to services that may need attention. Insights: Rating of neg/pos message about jetBlue Proof of Hypothesis: Areas of Recommendation: Cross analyze NLP results from Twitter with User Feedback
By sending a GET with a query parameter at the database's URL, the backend queries twitter to search for the specified term with some specification to provide more accurate results. The backend then utilizes natural language processing to create a list of top customer concerns.
This is the Critical Business Brand Barometer!
PythonAnywhere with hosted on a server with Google Cloud Language (NLP), Client using ReactJS, Bootstrap, Material UI, and Axios.
React package management, language versioning, string JSON formatted with ‘’ and “”
With little experience and all being first-time MLH hackers, we're proud of the integration of the frontend with the backend, figuring out new languages, and creating a demo.
New languages, creating and making API calls, limitations of NLP, how to work with people you’ve just met
Bettering the algorithms and NLP, analyze the target of negative sentiment, twist complaints into problem statements, aggregate data into a summary for clients.
This project was bootstrapped with Create React App.
In the project directory, you can run:
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Open http://localhost:3000 to view it in the browser.
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See the section about running tests for more information.
Builds the app for production to the build folder.
It correctly bundles React in production mode and optimizes the build for the best performance.
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Your app is ready to be deployed!
See the section about deployment for more information.
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If you aren’t satisfied with the build tool and configuration choices, you can eject at any time. This command will remove the single build dependency from your project.
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