We wanted a way to better understand social topics by aggregating user feelings.

What it does

Use cases:

  • As a daily consumer, I can analyze tweets and comments so that I can understand how others feel about a topic.
  • As a business owner, I can analyze tweets and comments related to my business so that I can understand my customers' sentiment towards them.

How we built it

Using a machine learning sentiment analysis algorithm, we scraped and analyzed large amounts of user sumbitted text and distinguished human feelings.

Challenges we ran into

  1. Supporting different website structures
  2. Chrome extension UI and limitations
  3. UI with dynamic text

Accomplishments that we're proud of

  1. Working with machine learning
  2. Shipping an MVP

What we learned

  1. Machine learning
  2. How to build chrome extensions
  3. Collaborative coding
  4. Production process
  5. How to ship something cool

What's next for Sentiment

  1. Expand the types of emotions.
  2. Applications to different types of websites.
  3. Detailed analytics and brand positioning suggestions
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