Analyzing Twitter Hashtag Impact using Neo4j, Python & JavaScript

The author of the text used Twitter data to create a demo that answers questions about users' conversations on specific topics. The demo uses Neo4j, a graph database, to store and query the Twitter data. The author designed a data model with nodes for users, tweets, hashtags, and countries, and relationships between them. They imported Twitter data into Neo4j using the python-twitter library and then visualized the results on a world map using Datamaps. The demo allows users to input a hashtag and view the top eight most used hashtags along with their impact in different countries. The author plans to add more features, such as selecting data from a given time frame and leveraging machine learning algorithms to find hidden patterns. They also started exploring Neo4j's new graph algorithms, including Connected Components and Strongly Connected Components.