## Inspiration

```
 We were inspired to work on this project to serve to aid the miscommunication that's all too present in our world today. Leaders, professors, and authority figures of all shapes and sizes throughout the ages have had to determine their audience's sentiment for one reason or another while they garnered support or searched for a solution. Single individuals simply do not have the time or ability to interview each person of the audience to find out what they all thought and are usually left to make guesses of how they felt by cheers of support or boos of hate. Our goal was to give those over arching figures another tool to observe their audience to ultimately gain a better understanding of their ideas, feelings, and sentiment.

```

## What it does

```
Sentithink attempts to solve this problem by using the concept of the Internet of Things to track, categorize, and visualize the audible sentiment of a geographically large group of people. Using multiple microphones dispersed throughout an area, Sentithink records 1 minute snippets of sound, transcribes it to text, parses out keywords, and tracks their frequencies over time. These frequencies, and their associated sentiments, are then visualized for the overseers to utilize to essentially see what's trending and how people are interpreting the event.

```

## How we built it

```
Sentithink was built in three main parts consisting of microphone client side code, a Web API backend in Azure, and a front end visualization built in javascript.

```

Client Side: Microphones on machines running Python script to send snippets to Azure API endpoint
Backend: Microsoft Azure Function API to record/produce results from speech to text
Frontend: Javascript utilization of d3.js to show relative frequencies

## Challenges we ran into

```
We weren't exactly sure how to use the microphones on the client side before hand but Google turned out to be a great resource. We did have some prior experience with Azure Cloud services but it seemed most of our trouble came from trying to visualize our data in javascript at the very end.

```

## Accomplishments that we're proud of

```
 We were able to set up all Azure aspects of our program:
     SQL Database
     Azure Function Web App
     Front end visualization
     Proof of concept demo
     Lightweight clientside app

```

## What we learned

```
We got a better understanding of Azure web services, javascript, and python utilization in a connect API driven environment

```

## What's next for Sentithink

```
 Get actual IoT devices to scale the product and test our product on a large area

```
## Inspiration

Imagine, for example, a situation where you're the manager of a call centre station, and you want to assure the quality of your workers' calls. But how do you measure the "quality" of each call? And how do you manage the quality of all the calls of all the workers (there may be a lot). This is where sensiment comes in. Tracking call sentiments has never been easier. Sensiment combines two key technologies to serve users a platform where they can input an audio stream and obtain the sentiment data of the audio. There are many applications to this technology beyond call centres, including personal data collection about calls (for example if you wanted to record the emotion level of all the people you are on a call with), and live emotion data collection.

## What it does

Sensiment displays live or recorded sentiment information in elegant visual charts to show the sentiment information extracted from audio, There are many applications to this which include call centre quality assurance, individuals to measure their emotions with their friends, and can detect domestic violence/abuse over text and/or live call.

## How we built it

We used google's speech-to-text API to convert calls/recorded media to text then utilized IBM's watson NLU api to get different sentiments from the call/text and provide the data in an elegant manner by creating a RESTful api. REST api was built using python flask library and the data was then sent to the frontend built using React JS.

## Challenges we ran into

We struggled with connecting the frontend and backend to a web socket to measure sentiment of a live video or voice call. We also struggled with managing the various APIs we used and connecting them all together into one application.

## Accomplishments that we're proud of

We were able to successfully complete the batch portion of the application and we were able to connect both APIs in a chain and achieved our goal.

## What we learned

We learned that live streaming of data is a very challenging idea and hopefully we find a better method to achieve this. We also learned a lot about the GCP API and we were introduced to the IBM NLU API.

## What's next for Sensiment

We plan to finish the live portion of our app so that users can stream data from their calls or from their microphone to and obtain live data about the emotions in their conversations.
## Inspiration

Too many times have broke college students looked at their bank statements and lament on how much money they could've saved if they had known about alternative purchases or savings earlier.

## What it does

SharkFin helps people analyze and improve their personal spending habits. SharkFin uses bank statements and online banking information to determine areas in which the user could save money. We identified multiple different patterns in spending that we then provide feedback on to help the user save money and spend less.

## How we built it

We used Node.js to create the backend for SharkFin, and we used the Viacom DataPoint API to manage multiple other API's. The front end, in the form of a web app, is written in JavaScript.

## Challenges we ran into

The Viacom DataPoint API, although extremely useful, was something brand new to our team, and there were few online resources we could look at We had to understand completely how the API simplified and managed all the APIs we were using.

## Accomplishments that we're proud of

Our data processing routine is highly streamlined and modular and our statistical model identifies and tags recurring events, or "habits," very accurately. By using the DataPoint API, our app can very easily accept new APIs without structurally modifying the back-end.

## What we learned

## What's next for SharkFin