This is documentation of a personal portfolio project demonstrating the practial use of data science, i.e. Sentiment Analysis, with Python, TextBlob for sentiment, and Tweepy to collect data from Twitter.

Sentiment Analysis refers to the use of natural language processing which is a subset of AI, and it is proven to be a useful method for effectively detecting expression in the text. Whether the said expression is negative or positive, it provides valuable insight for many companies and brands.

The Algorithm can be improved to gather data based on their geo/demographics.

Tools include: TextBlob, Tweepy

• UserInput

Will demonstrate the use of sentiment analysis performed on a simple user based input. The output is then a polarity ranging from -1.0 to 1.0, which can then be interpreted as either positive or negative feedback. Run Code

• Tweepy

Will incorporate the use of Twitter's API to gather the text-based off an inputted search keyword.