In the era of internet and social media, we need to process a large amount of information everyday. It would be useful to find a quick way to extract peoples' sentiments on certain topics.

What it does

The web app uses the latest google natural language machine learning model to analyze data feeds from both social media and news website from around the world. Detailed sentiment analysis with bar chart and pie chart will be displayed in the result section.

Major use cases include gauging public opinion on a variety of topics such as events (eg. nwHacks), political candidates, new products, etc. without having to conduct formal surveys.

How we built it

We used the google natural language API to analyze sentiments and the social media and news API to gather source data. We used the framework of html/python/flask.

Challenges we ran into

The challenge was to integrate different APIs and make them talk with each other. Creating a web app from scratch was also an issue as none of us have web development experience.

Accomplishments that we are proud of

The is our first hackathon and we are excited to get something done with a machine learning model.

What's next for SNARL

We want to extend our analysis to include more parameters and add the option of selecting different sources.

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