Why do some open source communities have more contributors? Does communication style play a part in attracting or repelling new developers? Using open source Natural Language Processing (NPL) libraries, we'll use sentiment analysis to determine the impact of negative and positive language on newcomers. This project dives into open source communities on github, scraping the github interactions with the githubv3.py open source library. Our project uses that data to classify github users as newcomers, involved contributors, or core contributors. We'll use NPL to determine the impact of communication choices on attracting and retaining open source contributors. Presenting such a large dataset can be challenging for many tools, so our project will use Mozilla Servo and Webrender, which have excellent performance for showing many HTML elements in a page, to visualize sentiment and participation. The ideal participant is someone interested in increasing engagement on a collaborative project and on visualizing challenging data. This session supports the Technology track.