QHacks 2017 - "Twinion" focuses on mining tweets from twitter's database and be able to mine for the "Pulse" on trending topics/products/people etc. Runs Andriod Lollipop 5.1, w/ Database api's
This project is based an idea of finding a better way to determine the “Pulse” or how the community of twitter really feel about trending topics on Twitter.
Our Hack involves Data Mining Twitter and performing Machine Learning/Natural Language Processing algorithms in order to abstract meaningful data from 140 characters or less.
Twinion is built using an Android App as the primary source of user experience to the end users. The logic of Twinion is created using Python as the primary programming language for Back End with Flask's framework processing data from Twitter API and utilizing Natural Language Processing from Indeco APIs. In addition to this a web application is built using javascript/html/css in order to show the idea of Twinion through Web Development.
Some challenges the team ran into included integration of both Front End and Back End of the project and creating the Android application version of the project.
Complete the logic of the implementation. Every member was able to learned new technologies and concepts. Team was able to work off each other’s skill sets while assisting one another on all aspects of the Hack. The team is proud of the completion of an Android App, Web app, Server and Machine Learning logic using Natural Language Processing The team learned about Bootstrap and the fundamentals of web development The team learned about Twitter and Indeco's APIs
Incorporation of a live Database with the possibility of utilizing the power of Amazon AWS Further improvements on the Android Application with add ons for further analysis utilizing Natural Language Processing and incorporating a test plan based on verifying and validating requirements of Twinion. Further Additions of the project would include a Dashboard to the Web Application portion which will show charts, graphs and tables representing twitter data and the results of Natural Language Processing algorithms with Indeco’s APIs