#TwitterSpeaks

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  Webpage takes in business type and city location to output the public's sentiment towards that type of business in that location.

Inspiration Utilize the prevalence of big data to help small businesses.

What it does: Displays public sentiment about the company of interest in a given target city through the analysis of twitter posts.

How we built it: We used the twitter api as well as an NLP library to generate polarity data for each filtered tweet.

Challenges we ran into: Connecting the backend to the frontend, creating an interactive experience for the user.

Accomplishments that we're proud of: We were able to successfully execute classification on our filtered dataset of tweets

What we learned: We learned about the versatility of a django server framework, as well as the vast applications of natural language processing

What's next for #TwitterSpeaks: We will attempt to introduce more algorithmic optimizations and greater scalability. Also, using the same concept to detect Fake News being spread on social media platforms.

Built With

• css
• django
• html5
• javascript
• python
• tweepy
• twitter-ads-api
• wordblob