Inspiration
We thought of people who are too lazy to respond to texts and meet other people.
What it does
It takes in live messages from conversation apps and analyzes the sentiment of the message to give a polarity rating of the phrase to see how positive or negative a message is. It then gives a response about the message based on the polarity.
How we built it
We used Twilio to receive and send messages. We used python and the Pattern library and the nltk library.
Challenges we ran into
There were not enough resources or experts on Twilio to help us.
Accomplishments that we're proud of
Were proud that we kept it together.
What we learned
We learned a lot about full stack development from the workshop. We also learned how to research APIs and implement them into our code.
What's next for Conversation Sentiment Automation
Creating a front end for our project to display the messages received and its associated sentiment value along with the automated response. Displaying a graph to show a sentiment history of a conversation (how negative or positive a conversation was).
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