Analyzing Social Network Interactions and Sentiment using Graphs

Graph databases are becoming increasingly important in data science, especially for use cases involving highly connected data. For July's meetup, we are honored to be hosting jointly with the Data Science MD meetup (https://www.meetup.com/Data-Science-MD/events/240982895/) a presentation on how the graph database Neo4j can be used to analyze social networks.

Agenda

6:30 PM -- Networking & Food

7:05 PM -- Greetings

7:15 PM -- Using Neo4j to Explore Topic-Based Communities in Social Networks - Laura Drummer

Talks

Using Neo4j to Explore Topic-Based Communities in Social Networks

Traditional social network analysis is performed on a series of nodes and edges, generally gleaned from metadata about interactions between several actors. In the intelligence and law enforcement communities, this metadata can frequently be paired with data and communications content. Our analytic, SocialBee, takes advantage of this widely untapped data source to not only perform more in-depth social network analysis based on actor behavior, but also enrich the social network analysis with topic modelling, sentiment analysis, and trending over time. Through extraction and analysis of topic-enriched links, SocialBee has also been able to successfully predict “hidden relationships”; i.e., relationships not seen in the original dataset, but that exist in an external dataset via different means of communication.

The clustering of communities based on behavior over time can be done by looking purely at metadata, but SocialBee also analyzes the content of communications which will allows for a richer analysis of the tone, topic, and sentiment of each interaction. Traditional topic modelling is usually done using natural language processing to build clusters of similar words and phrases. By incorporating these topics into a communications network stored in neo4j, we are able to ask much more meaningful questions about the nature of individuals, relationships, and entire communities.

Using its topic modelling features, SocialBee can identify behavior based communities within this networks. These communities are based on relationships where a significant percentage of the communications are about a specific topic. In these smaller networks, it is much easier to identify influential nodes for a specific topic, and find disconnected nodes in a community.

This talk explores the schema designed to store this data in neo4j, which is loosely based on the concept of the “Author-Recipient-Topic” model as well as several advanced queries exploring the nature of relationships, characterizing sub-graphs, and exploring the words that make up the topics themselves.

Speaker

Laura Drummer (https://www.linkedin.com/in/laura-drummer-b382a615/) is an experienced technical consultant who truly believes in the philosophy of “mission first” analysis. She is a strong believer in the necessity of continuous research and education to keep ones technical capabilities sharp and relevant to an always changing target environment. She is also devoted to strengthening, and making more visible, the network of women technical analysts in the Intelligence Community. Laura has over 14 years of experience in intelligence analysis, data analytics, and software development. In addition to technical analysis, Laura serves as the Director of Software and Engineering in Novetta’s Cyber Operations Division.

Laura holds a MS in Information Systems from University of Maryland Baltimore County and a BA in Mandarin Chinese from Saint Mary’s College of Maryland. Laura lives in Maryland with her husband and two adorable dogs. She enjoys cooking, board games, travel, and is expecting her first baby in November of this year.