Tweet2Stock

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Inspiration

One of our friends previously took a data visualization class and wanted to explore more with stocks and Twitter.

What it does

This python program that analyzes recent tweets that mentions any publicly traded companies and launches a sentiment analysis to determine whether the aggregate opinion of the company in question is positive or negative for that time period.

How we built it

The code is written in Python and its entity detection and sentiment analysis is done using Google's Cloud Natural Language API and the Wikidata Query Service provides the company data.

Challenges we ran into

Initially had difficulties utilizing all of the API's together, but we were able to figure it out!

Accomplishments that we're proud of

The software successfully connects and utilizes the Wikidata Query Service, the Twitter API, and Google Natural Language API in order to generate a average sentiment value pertaining to a given company over a certain amount of tweets.

What we learned

How to connect to the Wikidata Query Service, the Twitter API, and Google Natural Language API. We also learned how to utilize Google Natural Language API in order to generate an average sentiment value for a given company.

What's next for Tweet2Stock

Implement a front-end that allows a user to select: multiple ticker symbols, a time-span, amount of tweets. Implement a back-end that generates useful charts after analyzation. Give the user the ability to save charts to .csv and other file formats and give an analyzation a name Save what was analyzed to the cloud Utilize the Enterprise Twitter API for more accurate historical data

Built With

• google
• google-cloud
• python
• twitter
• wiki-data