PeopleMap

Visualization Tool for Mapping Out Researchers using Natural Language Processing

Main Page of PeopleMap

About PeopleMap

PeopleMap is an interactive, web-based tool that uses natural language processing (NLP) techniques to improve the accuracy of the information and characterization of researchers. The tool maps out researchers through textual embeddings that make use of a variety of textual characteristics related to each researcher, such as college/school of their professorship, laboratory affiliation, titles and abstracts of their papers found on Google Scholar, and Google Scholar keywords.

Using the following steps, you will be to visually explore the research interests of any group of researchers with Google Scholar profiles. It will walk you through the steps of scraping Google Scholar profiles, processing the data, and exploring the visualizations generated by PeopleMap.

• To access two live demos of PeopleMap, please go to either of the following links:

  • https://poloclub.github.io/people-map/ideas/

  • https://poloclub.github.io/people-map/ml/

• To access the Github repository, please go to the following link:

  • https://github.com/poloclub/people-map

PeopleMap is brought to you by Jon Saad-Falcon, Omar Shaikh, Jay Wang, Austin Wright, Sasha Richardson, and Polo Chau.

Getting Started

• To begin the process of setting up PeopleMap, navigate to the page below:

Getting Started

Setting Up PeopleMap Platform

• To jump to the steps for data collection and loading the PeopleMap platform, navigate to the page below:

Setting up PeopleMap

Functionalities in PeopleMap

• For more information on the functionalities of PeopleMap, navigate to the page below:

Functionalities in PeopleMap

FAQ

• For any questions about PeopleMap, debugging issues, and contacting the creators, navigate to the page below:

FAQ