ColaHeat

Inspiration

We wanted to use multiple APIs and Machine Learning algorithms for texts analysis. We decided on sentiment analysis from tweets.

What it does

Display general sentiment of social media activity in the Columbia area in the form of a heat map.

How we built it

• Database MongoDB

• Data parser Python 2.7 Tweepy - python client for twitter api Textblob - python library for processing textual data

• Map Display Expressjs Javascript HTML Bootstrap

Challenges we ran into

• Parsing the JSON data from the inital data collection
• AJAX async requests
• Displaying the geolocation data on the heat map

Accomplishments that we're proud of

• A complete working proof of concept
• Real heat map data
• Decent design
• Had fun :)

What we learned

• Charting and mapping geo-data
• Parsing twitter api data
• Running sentiment analysis
• Refresh on JavaScript

Whats next for ColaHeat

• Taking it to a global scale
• More data for analysis

Built With

• ajax
• bootstrap
• chart.js
• css3
• express.js
• google-maps
• html5
• javascript
• mongodb
• python
• textblob
• tweepy