## Inspiration

JetBlue challenge of YHack

## What it does

Website with sentiment analysis of JetBlue

## How I built it

Python, Data scraping, used textblob for sentiment analysis

## Challenges I ran into

choosing between textblob and nltk

## Accomplishments that I'm proud of

Having a finished product

## What I learned

How to do sentiment analysis

## What's next for FeelingBlue
## Inspiration

We got our inspiration from looking at the tools provided to us in the Hackathon. We saw that we cold use the Google API’s effectively when analyzing the sentiment of the customers review on social media platforms. With the wide range of possibilities, it gave us we got the idea of using programs to see the data visually

## What it does

JetBlueByMe is a program which takes over 16000 reviews from trip advisor, and hundreds of tweets from twitter to present them in a graphable way. The first representation is an effective yet simple word cloud which shows more frequently described adjective larger. The other is a bar graph to show which word appears most consistently.

## How we built it

The first step was to scrape data off multiple websites. To do this a web scraping robot by UiPath was used. This saved a lot of time and allowed us to focus on other aspects of the program. For Twitter, Python had to be used in junction with Beautiful Soup library to extract the tweets and hashtags. This was only possible after receiving permission 10 hours after applying to Twitter for its API use. The Google sentiment API and Syntax API were used to create the final product. The syntax API helped extract the adjectives from the reviews so we can show a word cloud. To display the word cloud, the programming was done in R as it is an effective language for data manipulation.

## Challenges we ran into

We were unable to initially use UiPath for Twitter to scrape data as it didn’t have a next button, so the robot did not continue on its own. This was fixed using beautiful soup on Python. Also, when trying to extract the adjectives, the compiling was very slow causing us to fall back about 2 hours. None of us knew the inns and outs of web hence it was a challenging problem for us.

## Accomplishments that we're proud of

We are happy about finding an effective way to word scrape using both UiPath and BeautifulSoup. Also, we weren't aware that Google provided an API for sentiment analysis, access to that was a big plus. We learned how to utilize our tools and incorporated them into our project. We also used Firebase to help store data on the cloud so we know its secure.

## What we learned

Word scraping was a big thing that we all learned as it was new to all of us. We had to extensively research before applying any idea. Most of the group did not know how to use the language R but we understood the basics by the end. We also learned how to set up a firebase and google-cloud service that will definitely be a big asset in our future programming endeavours.

## What's next for JetBlueByMe

Our web scraping application can be optimized and we plan on getting a live feed set up to show reviews sentiment in real-time. With time and resources, we would be able to implement that.
## Inspiration

Given the increase in mental health awareness, we wanted to focus on therapy treatment tools in order to enhance the effectiveness of therapy. Therapists rely on hand-written notes and personal memory to progress emotionally with their clients, and there is no assistive digital tool for therapists to keep track of clients’ sentiment throughout a session. Therefore, we want to equip therapists with the ability to better analyze raw data, and track patient progress over time.

## Our Team

* Vanessa Seto, Systems Design Engineering at the University of Waterloo
* Daniel Wang, CS at the University of Toronto
* Quinnan Gill, Computer Engineering at the University of Pittsburgh
* Sanchit Batra, CS at the University of Buffalo

## What it does

Inkblot is a digital tool to give therapists a second opinion, by performing sentimental analysis on a patient throughout a therapy session. It keeps track of client progress as they attend more therapy sessions, and gives therapists useful data points that aren't usually captured in typical hand-written notes.

Some key features include the ability to scrub across the entire therapy session, allowing the therapist to read the transcript, and look at specific key words associated with certain emotions. Another key feature is the progress tab, that displays past therapy sessions with easy to interpret sentiment data visualizations, to allow therapists to see the overall ups and downs in a patient's visits.

## How we built it

We built the front end using Angular and hosted the web page locally. Given a complex data set, we wanted to present our application in a simple and user-friendly manner. We created a styling and branding template for the application and designed the UI from scratch.

For the back-end we hosted a REST API built using Flask on GCP in order to easily access API's offered by GCP.
Most notably, we took advantage of Google Vision API to perform sentiment analysis and used their speech to text API to transcribe a patient's therapy session.

## Challenges we ran into

* Integrated a chart library in Angular that met our project’s complex data needs
* Working with raw data
* Audio processing and conversions for session video clips

## Accomplishments that we're proud of

* Using GCP in its full effectiveness for our use case, including technologies like Google Cloud Storage, Google Compute VM, Google Cloud Firewall / LoadBalancer, as well as both Vision API and Speech-To-Text
* Implementing the entire front-end from scratch in Angular, with the integration of real-time data
* Great UI Design :)

## What's next for Inkblot

* Database integration: Keeping user data, keeping historical data, user profiles (login)
* Twilio Integration
* HIPAA Compliancy
* Investigate blockchain technology with the help of BlockStack
* Testing the product with professional therapists