In the modern age, data is readily available everywhere on the internet. Therefore, it is a good idea to harness that data and generate useful information

What it does

Twitter-scraper fetches tweets related to a topic on Twitter and uses sentiment analysis to determine each tweets' opinion on the topic and then calculates the prevailing opinion on that topic by calculating the average of each tweets' sentiment value

How we built it

We used JavaScript and the React.js framework to build a user-friendly, simple-to-use website interface for Twitter-scraper. Then, we used Python and Flask to build the backend which handles the bulk of scraping Twitter and analyzing data. In particular, we used the Snscrape library to interact with Twitter's API and used the VADER library to perform sentiment analysis on the tweets retrieved from Twitter.

Challenges we ran into

Many team members have other work to attend to during the hackathon. Thus, we could not contribute a lot of development effort into the project.

Accomplishments that we're proud of

Despite the limited efforts we could contribute to the project, we were still able to build the project into a decent and complete product, with a relatively aesthetic frontend and a backend that completes the app specification with no errors.

What we learned

This is our first time working with React.js, so we learned about React. We also learned how important it is to plan personal activities carefully to not interfere with work

What's next for twitter-scraper

We are planning for new functionalities for twitter-scraper, including more advanced analytics on tweet data, more places to scrape data from, and perhaps some analytics functions specific to fintech and trading

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