Description as a Tweet:

Simplifies the process of reading reviews on Yelp, with more specific options to see what people like and dislike about restaurants near a given location.

Inspiration:

A strong interest in web scraping and simplifying consumption of existing website features.

What it does:

Scrapes reviews from Yelp, given a city and state, and presents bar graphs that allow you to immediately tell how well or poorly a restaurant was received by reviewers.

How we built it:

We utilized python requests and python web scraping libraries to gather data and determine word frequency per review or overall, as well as word sentiments acquired from Textblob to determine whether the reviewer had a positive or negative experience at the restaurant.

Technologies we used:

  • HTML/CSS
  • C/C++/C#
  • Python
  • Flask
  • Misc

Challenges we ran into:

Getting the know the structure of the HTML pages, as the classes used for the tags changed a full day into our development.

Accomplishments we're proud of:

Teaching everyone on our team how to use Python for the first time. Helping them become familiar with various web scraping libraries. Setting them up SourceTree for their GitHub accounts to encourage constant engagement with version control tools such as GitHub. Resolving merge conflicts. Working in a completely new language and learning how to do web scraping all in the same 36 hours. Successfully completing our Hackathon project.

What we've learned:

How to be a project manager, how to teach others, how to network, and how to resolve merge conflicts.

What's next:

User Agent header implementation, as well as CSS and .svg graph styling.

Built with:

Visual Studio Code, SourceTree, GitHub, and our laptops.

Prizes we're going for:

  • Best UiPath Automation Hack (MLH)
  • Best Web App

Team Members

Lucnalie Jironvil
Collin Strassburg
Myron Lacey
Jonathan Jironvil

Table Number

Table 58

View on Github