Eat Your Feelings DB CLI

Eat Your Feelings DB (EYF) is a simple command line project that Edward N, and I developed for our Module One final project at the Flatiron school in 2018. EYF accesses restaurant reviews via the Google places API and associates each review with an emotional analysis from Parallel Dots. We built an OO model and active record database to select for restaurants and reviewers based on the emotional content of their respective reviews.

This app can be forked on GitHub and cloned to a local machine. To re-seed the database drop the existing tables and re-migrate. Enter Parallel Dots and Google Places API keys in the appropriate spots in the db/seeds.rb file. If you’d like to analyze different restaurants replace the strings in the “Places” array with alternative Google Place IDs. Once you have done this run “bundle install” then “rake db:seed” in terminal and it should populate your database with reviews and emotional analysis. Then you should be able to enter “Ruby bin/run.rb” in terminal and launch our application. Enter “all restaurants” to confirm the data was properly entered.

We would value any contributions in areas of user interface, automatically selecting / adding new restaurants, and performing more analytical operations on the dataset.

We ran into a few interesting things while working on this project. First we spent a while looking through different APIs and the values that they returned to find what would fit our concept the best. Originally we envisioned using Yelp and IBM Watson but both APIs proved too restrictive and too generalized. Parallel Dots and Google Places on the other hand worked very well with some minor restrictions, mainly Google Places limits each query to 4 reviews which was fine for our purposes but may limit more rigorous analysis.

Another API related challenge we are still working on is how to manage API keys while regularly pushing to GitHub. We ultimately had to reset our API keys after accidentally making them public. Attempts to undo this were frustrating and we still have a lot to learn about protecting credentials like this. [Update: We learned how to use gitignore!]

Lastly, building the interface and having a good framework for what our methods should be returning was a recurring issue. In some cases we wanted the object methods “puts-ing” text to the screen other times we wanted a second helper method to format the output for the user. It was hard to figure out how to make this consistent and what our data types should be. There is probably a lot of room for improvement here too. Ultimately we are happy with our final product and think that EYF represents a great proof of concept for how machine learning and sentiment analysis can be applied.

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