tweeterTrends

In this project, I developed a geographic visualization of Tweeter data across the United States of America using lists, dictionaries, and data abstraction techniques to create a modular program.

At the end, the program will be able to draw how different states feel about Texas and this is going to be through:

• Collecting public Twitter posts (tweets) that have been tagged with geographic locations and filtering for those that contain the "texas" query term
• Assigning a sentiment (positive or negative) to each tweet, based on all of the words it contains
• Aggregating tweets by the state with the closest geographic center, and finally
• Coloring each state according to the aggregate sentiment of its tweets. #f03c15 means positive sentiment; #1589F0 means negative

Usage

I am going to querry the following commands. Remember that #f03c15 == positive sentiment and #1589F0 == negative sentiment

To know how people feel about Texas

python3 trends.py -m texas

To know how people feel about Sandwiches

python3 trends.py -m sandwiches

To know how people feel about the Former President Obama

python3 trends.py -m obama

Acknowledgements

Aditi Muralidharan developed this project with John DeNero. Hamilton Nguyen extended it. Keegan Mann developed the autograder. Many others have contributed as well.