Data Science

Live Data Analysis Using Twitter

Text Mining, Data Cleaning, Tokenization, Fitting Deep Learning Model, Real Time Data

A sentiment analysis for real-time Twitter events.

I developed this analytical tool to understand trending topics and the attitudes towards ideas or products. This tool has the ability to listen to specific keywords and develop a sentiment analysis of the feed. One applicaiton of this data could be to feature site content that is relevant.

Using REST APIs provided by Twitter, I interacted with their service to access the data needed for this project. I set up the customization to stream tweets based on keywords. The collected data is stored using a relational database.

I felt equal parts like a snoop and a treasure hunter.

I'm going to roll with treasure hunter. I truly found gems! This information is extremely useful and a value for almost any business.

Used: Tweepy, SQL, Python, Plotly, Dash

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